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Schedule of FOSS4G Europe 2025 Mostar, 30.06.2025
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schedule.xml
<?xml version="1.0" encoding="UTF-8"?>
<!-- Made with love by pretalx v2024.1.0. -->
<schedule>
<generator name="pretalx" version="2024.1.0"/>
<version>0.14</version>
<conference>
<title>FOSS4G Europe 2025</title>
<acronym>foss4g-europe-2025</acronym>
<start>2025-07-16</start>
<end>2025-07-18</end>
<days>3</days>
<timeslot_duration>00:05</timeslot_duration>
<base_url>https://talks.osgeo.org</base_url>
<logo>https://talks.osgeo.org/media/foss4g-europe-2025/img/logo_7tqODhg.jpeg</logo>
<time_zone_name>Europe/Sarajevo</time_zone_name>
<track name="Academic track" slug="311-academic-track" color="#3c23b8"/>
<track name="Panel Discussion" slug="293-panel-discussion" color="#2b6056"/>
<track name="State of software" slug="215-state-of-software" color="#cd8b76"/>
<track name="Open Data" slug="216-open-data" color="#090808"/>
<track name="Open standards and interoperability for geospatial" slug="222-open-standards-and-interoperability-for-geospatial" color="#ced306"/>
<track name="FOSS4G ‘Made in Europe’" slug="221-foss4g-made-in-europe" color="#1588d3"/>
<track name="Open community" slug="219-open-community" color="#525480"/>
<track name="FOSS4G in education and research" slug="218-foss4g-in-education-and-research" color="#b93580"/>
<track name="Building a business with FOSS4G" slug="223-building-a-business-with-foss4g" color="#7b00be"/>
<track name="Transition to FOSS4G" slug="220-transition-to-foss4g" color="#81861b"/>
<track name="Use cases &amp; applications" slug="217-use-cases-applications" color="#f61449"/>
<track name="Birds of a Feather (BoF)" slug="224-birds-of-a-feather-bof" color="#2b6056"/>
<track name="Keynote" slug="225-keynote" color="#2b6056"/>
<track name="Plenary" slug="227-plenary" color="#2b6056"/>
</conference>
<day index="1" date="2025-07-16" start="2025-07-16T04:00:00+02:00" end="2025-07-17T03:59:00+02:00">
<room name="KOS" guid="76dfe3d7-bde5-5712-8dd5-f2b92782aa23">
<event guid="9c3fd998-7a5c-5d54-ad95-27fb881593a8" id="3702">
<room>KOS</room>
<title>Opening Ceremony</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T09:00:00+02:00</date>
<start>09:00</start>
<duration>00:30</duration>
<abstract>Let's get this festival started.
Welcome remarks from the FOSS4G Europe 2025 chair, city of Mostar, and University of Mostar representatives.</abstract>
<slug>foss4g-europe-2025-3702-opening-ceremony</slug>
<track>Plenary</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/YLEXT7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/YLEXT7/feedback/</feedback_url>
</event>
<event guid="fa5bf36a-b670-55b0-b87b-07a945277e52" id="3722">
<room>KOS</room>
<title>What Lawyers Need from You: Making Geospatial Technology Work for Criminal Justice</title>
<subtitle/>
<type>Keynote</type>
<date>2025-07-16T09:30:00+02:00</date>
<start>09:30</start>
<duration>00:45</duration>
<abstract>Rapidly evolving technology has provided more ways than ever before to document serious international crimes, including war crimes, crimes against humanity, and genocide. Geospatial technologies—particularly satellite imagery—can provide detailed insights into crimes committed in conflict areas that are otherwise difficult or impossible to investigate. Numerous international criminal cases have featured this digital evidence, helping to secure accountability for atrocities.
As with any form of evidence in a criminal trial, certain legal standards must be met. Evidence must be shown to be authentic (verifiable and unaltered) and reliable (accurate and dependable)—otherwise judges will refuse to admit it. Meeting these standards for digital evidence requires communication between lawyers and technologists. Lawyers must explain legal requirements for evidence; technologists must clarify what their systems can deliver and how technical processes ensure data integrity and accuracy.
This keynote will advance this communication by outlining core principles for maximising the evidentiary value of geospatial data. By keeping these principles in mind, those working in the collection and processing of geospatial data, or in the development of geospatial technologies, can help ensure that today’s data meets tomorrow's courtroom standards. The objective is to identify what makes data more valuable as evidence and translate this into practical technical requirements.</abstract>
<slug>foss4g-europe-2025-3722-what-lawyers-need-from-you-making-geospatial-technology-work-for-criminal-justice</slug>
<track>Plenary</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VD98XE/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VD98XE/feedback/</feedback_url>
</event>
</room>
<room name="EL11" guid="e2a70c93-299e-589b-b24b-95b372b81974">
<event guid="19141590-e638-5be8-af36-2a150853bd58" id="3116">
<room>EL11</room>
<title>QGIS Feature Frenzy - What's new in the current LTR (3.40) and Latest Releases (3.42 &amp; 3.44)</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>QGIS releases three new versions per year and each spring a new long-term release (LTR) is designated. Each version comes with a long list of new features. This rapid development pace can be difficult to keep up with, and many new features go unnoticed. This presentation will give a visual overview of some of the most important new features released over the last calendar year.
In March of 2025 a new Long-term release was published (3.40), and shortly before FOSS4G, the latest stable version of QGIS (3.44) will be released. I will start by comparing the new LTR (3.40) to the previous (3.34). Here I will highlight some of the most important new features found in the latest LTR. I will then turn my attention to the most important new features found in the latest releases (3.42 &amp; 3.44).
Each highlighted feature will not simply be described, but will be demonstrated with real data. The version number for each feature will also be provided. If you want to learn about the current capabilities of QGIS, this talk is for you!
Potential topics include: GUI enhancements * Symbology * Point cloud support * Data Providers * Processing * 3D * Editing</abstract>
<slug>foss4g-europe-2025-3116-qgis-feature-frenzy-what-s-new-in-the-current-ltr-3-40-and-latest-releases-3-42-3-44-</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/P7UDFC/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/P7UDFC/feedback/</feedback_url>
</event>
<event guid="0f91fb5c-39c6-5357-84a5-df78e78fe474" id="3386">
<room>EL11</room>
<title>State of GeoServer</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>GeoServer is a web service for publishing your geospatial data using industry standards for vector, raster and mapping, as well as to process data, either in batch or on the fly.
GeoServer powers a number of open source projects like GeoNode and geOrchestra and it is widely used throughout the world by organizations to manage, disseminate and analyze data at scale.
This presentation provides an update on our community as well as reviews of the new and noteworthy features for the latest releases. In particular, we will showcase all the new features landed in the 2.26 and 2.27 series.
Attend this talk for a cheerful update on what is happening with this popular OSGeo project, whether you are an expert user, a developer, or simply curious what GeoServer can do for you.</abstract>
<slug>foss4g-europe-2025-3386-state-of-geoserver</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/SGSRKF/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/SGSRKF/feedback/</feedback_url>
</event>
<event guid="7c85ec97-1663-51ee-b1a3-4e40ade98134" id="3504">
<room>EL11</room>
<title>State of GDAL: what's new in 3.10 and 3.11?</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>We will give a status report on the GDAL software, focusing on recent developments and achievements in the 3.10 and 3.11 GDAL versions released during the last year.
The discussed topics will be as various as the scope of GDAL is, covering among other things:
- Adding a "gdal" front-end command line interface, to give a consistent and flexible user experience, as an outcome of the user survey the project conducted end of 2024
- Enhancements in the VRT (Virtual) raster driver to support computation of arbitrary expressions
- gdal_viewshed enhancements
- support for STAC GeoParquet in the GDAL Tile Index driver
- A new vector driver ADBC (Arrow Database Connectivity) driver, in particular with support for DuckDB or Parquet datasets
- LiberTIFF: an alternate native thread-safe read-only GeoTIFF reader
- Support for embedding resource files into the library
- Addition of a OGR_SCHEMA open option to selected OGR drivers</abstract>
<slug>foss4g-europe-2025-3504-state-of-gdal-what-s-new-in-3-10-and-3-11-</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/GRQAEC/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/GRQAEC/feedback/</feedback_url>
</event>
<event guid="e6daff8a-4b11-5b0a-a31d-6483e0c81677" id="3162">
<room>EL11</room>
<title>MapLibre projects, in one status update</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>Present everything MapLibre community has been working on, including tile serving, fonts and sprite handling, to visualizations for both web and native, to new types of tools and format standards.</abstract>
<slug>foss4g-europe-2025-3162-maplibre-projects-in-one-status-update</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/G7WVWY/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/G7WVWY/feedback/</feedback_url>
</event>
<event guid="68414cbc-3466-57b6-8f74-afb3038316a0" id="3225">
<room>EL11</room>
<title>pygeoapi project status</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>pygeoapi is an OGC API Reference Implementation. Implemented in Python, pygeoapi supports numerous OGC APIs via a core agnostic API, different web frameworks (Flask, Starlette, Django) and a fully integrated OpenAPI capability. Lightweight, easy to deploy and cloud-ready, pygeoapi's architecture facilitates publishing datasets and processes from multiple sources. The project also provides an extensible plugin framework, enabling developers to implement custom data adapters, filters and processes to meet their specific requirements and workflows. pygeoapi also supports the STAC specification in support of static data publishing.
pygeoapi has a significant install base around the world, with numerous projects in academia, government and industry deployments. The project is also an OGC API Reference Implementation, lowering the barrier to publishing geospatial data for all users.
This presentation will provide an update on the current status, latest developments in the project, including new core features and plugins. In addition, the presentation will highlight key projects using pygeoapi for geospatial data discovery, access and visualization.</abstract>
<slug>foss4g-europe-2025-3225-pygeoapi-project-status</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/EPWGP7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/EPWGP7/feedback/</feedback_url>
</event>
<event guid="2836c3ab-1c61-52ea-a27e-238f84f7a4d1" id="3385">
<room>EL11</room>
<title>State of GeoNode</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>This presentation will introduce the attendees to GeoNode's new capabilities. We will provide a summary of the new features added to GeoNode in the last release together with a glimpse of what we have planned for next year and beyond, straight from the core developers.</abstract>
<slug>foss4g-europe-2025-3385-state-of-geonode</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/MAWNKK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/MAWNKK/feedback/</feedback_url>
</event>
<event guid="92df48fb-c2e7-5490-b953-e10b2d6b4053" id="3510">
<room>EL11</room>
<title>News from the OSGeoLive project &amp; Challenges</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>OSGeoLive is a self-contained bootable DVD, USB thumb drive or Virtual Machine based on Lubuntu, that allows you to try a wide variety of open source geospatial software without installing anything. It is composed entirely of free software, allowing it to be freely distributed, duplicated and passed around. It provides pre-configured applications for a range of geospatial use cases, including storage, publishing, viewing, analysis and manipulation of data. It also contains sample datasets and documentation. OSGeoLive is an OSGeo project used in several workshops at FOSS4Gs around
the world.
The OSGeoLive project has contributions from many projects for over a decade. How are technology changes affecting OSGeoLive? Where is OSGeoLive heading and what are the challenges and opportunities for the future? Let's have a closer look.
- Project page https://live.osgeo.org</abstract>
<slug>foss4g-europe-2025-3510-news-from-the-osgeolive-project-challenges</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8M7Q7W/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8M7Q7W/feedback/</feedback_url>
</event>
<event guid="1100f08d-bab3-578f-8756-62608bab953e" id="3227">
<room>EL11</room>
<title>pycsw project status</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>pycsw is an OGC API - Records and OGC CSW server implementation written in Python. Started in 2010 (more formally announced in 2011), pycsw allows for the publishing and discovery of geospatial metadata via numerous APIs (CSW 2/CSW 3, OpenSearch, OAI-PMH, SRU), providing a standards-based metadata and catalogue component of spatial data infrastructures. pycsw is Open Source, released under an MIT license, and runs on all major platforms (Windows, Linux, Mac OS X).The project is certified OGC Compliant, and is an OGC Reference Implementation.
The project currently powers numerous high profile catalogues such as EOEPCA, IOOS, NGDS, NOAA, US Department of State, US Department of Interior, geodata.gov.gr, Met Norway and WMO WOUDC. This session starts with a status report of the project, followed by an open question answer session to give a chance to users to interact with members of the pycsw project team. This session will cover how the project PSC operates, the current project roadmap, and recent enhancements focused on ESA's EOEPCA, Open Science Data Catalogue and OGC API - Records.</abstract>
<slug>foss4g-europe-2025-3227-pycsw-project-status</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/E3UX7B/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/E3UX7B/feedback/</feedback_url>
</event>
<event guid="bd615380-9ade-5b1b-b448-fdc467e2b6a0" id="3396">
<room>EL11</room>
<title>GeoServer 3 Status Report: How We Got Here, How It’s Going</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>This presentation provides an in-depth status update on GeoServer 3, the ambitious overhaul of the widely used open-source server for spatial data and web services. Announced as part of a community-driven crowdfunding effort, GeoServer 3 seeks to modernize the platform’s foundation to ensure it meets the growing demands of the geospatial community.
We’ll first analyze the GeoServer 2.x status quo, and the effect of cascading changes that a “simple” Spring upgrade caused, turning the activity into a cross project overhaul, and how the large effort required got socialized and eventually brought to implementation via in-kind volunteering and a crowdfunding campaign driven by Camp2Camp, GeoCat and GeoSolutions.
We will explore the planned milestones in the transition to GeoServer 3. These include critical refactorings, such as replacing aging libraries, adopting modern Java frameworks, and integrating support for the latest versions of GeoTools and GeoWebCache. Key technical advancements include the evolution and integration of ImageN for improved raster data processing, the migration from Wicket 7 to Wicket 10 for a modernized and more secure web user interface, and the adoption of Jakarta EE and Spring 6 to support enhanced security, scalability, and long-term compatibility with modern Java ecosystems.
Join us to investigate progress, reflect on the lessons learned, and get inspired by what’s possible with GeoServer 3—a project that continues to empower geospatial professionals and organizations worldwide.</abstract>
<slug>foss4g-europe-2025-3396-geoserver-3-status-report-how-we-got-here-how-it-s-going</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/HF8U3F/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/HF8U3F/feedback/</feedback_url>
</event>
</room>
<room name="SA01" guid="b2c2c705-1015-5f82-b5c4-38f381b9eb7c">
<event guid="fd1b3328-007a-5b59-b647-66dbe147eb62" id="3402">
<room>SA01</room>
<title>Georeferencing and publishing Finnish historical maps</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>More than 30000 scanned historical maps from the archive of the National Land Survey of Finland were georeferenced and made available as open data through download services. The oldest maps which were georeferenced in this project are from 1860s. Maps until 1917 cover the area of the Grand Dutchy of Finland in the Russian Empire, and since 1917 the area of the independent Republic of Finland.
All the georeferenced maps were originally produced into some regular mapsheet grid. In theory this makes georeferencing easy because the four corner points of a mapsheet can be used as ground control points, and the georeferenced coordinates are known by the grid. However, during the period 1860-2020 about 15 different mapsheet grids and 5 different coordinate systems have been used and most of them were not available as spatial data in a digital format. Also, the map makers have not always honored strictly the mapsheet grid which make issues for automatic processing.
The georeferenced maps were published in Cloud Optimised GeoTIFF format in two versions. One version preserves the whole information of the scanned map and contains the original marginalia and possible handwritten remarks, while the other version is clipped to the area of the actual map for making it easy to build seamless mosaics from adjacent map sheets. Maps were published through the Geoportti Research Infrastructure that is particularly targeted for researchers, teachers, and students, but that is open for everybody. Maps can be downloaded with a web application or as mass download by using HTTP, FTP, or rsync. Maps can also be searched and downloaded with STAC.
In this talk the general workflow of georeferencing historical maps is presented with examples about typical oddities in historical maps which can make the workflow fail. Reasons are also given for the decisions that were made about the file format, compression, and image metadata.</abstract>
<slug>foss4g-europe-2025-3402-georeferencing-and-publishing-finnish-historical-maps</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/CYVEPS/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/CYVEPS/feedback/</feedback_url>
</event>
<event guid="d84e3dbe-d794-5f8d-9c3a-56dacd72010b" id="3344">
<room>SA01</room>
<title>Customizing QGIS for Forestry Operations: A User-Friendly Approach for Non-GIS Professionals</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>At the Danish Nature Agency, we have streamlined the QGIS interface to include only essential tools while integrating additional functionalities using locale plugins. This customization ensures the protection of cultural heritage sites, flora, and fauna during forestry operations. Our approach prioritizes usability for non-GIS professionals, enabling intuitive navigation and efficient decision-making in the field.
This presentation will provide a walkthrough of our adapted QGIS interface, highlighting the local plugins that enhance accessibility. I will showcase the implemented solutions that simplify spatial data interaction while ensuring critical environmental and cultural heritage considerations are met. Attendees will gain insights into how open-source GIS can be tailored for real-world applications where ease of use is paramount.</abstract>
<slug>foss4g-europe-2025-3344-customizing-qgis-for-forestry-operations-a-user-friendly-approach-for-non-gis-professionals</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/TCGSUJ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/TCGSUJ/feedback/</feedback_url>
</event>
<event guid="3a029aff-f842-5880-95ef-2382d0eed0ec" id="3390">
<room>SA01</room>
<title>Our experience with Mergin Maps: An open-source solution for field data</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Mergin Maps Community edition is an open-source solution made in Europe that enables efficient spatial data collection on mobile devices. Its integration with QGIS allows for easy synchronization between mobile and desktop GIS, making it easy to implement even complex field data collection workflows, especially if you are already familiar with QGIS.
We have successfully implemented Mergin Maps for various organizations and projects across France and Switzerland, each with distinct requirements and challenges. Our role spans the entire implementation process, from consulting on data needs and designing data models to configuring workflows and deploying the Mergin Maps server in a cloud infrastructure. The self-hosted approach gives us full control over data security, performance, and customization. Mergin Maps plays an important role in our mission to support public administrations in adopting open-source solutions. The synergy between Mergin Maps and other FOSS tools like QGIS and PostgreSQL helps demonstrate the power of open-source software, making it easier for public organizations to transition away from proprietary systems.
While we are not the core developers of Mergin Maps, we were able to make some improvements to this promising project. In this talk, we will share our hands-on experience, the benefits we’ve observed, and the lessons learned from using Mergin Maps in real-world field data collection scenarios.</abstract>
<slug>foss4g-europe-2025-3390-our-experience-with-mergin-maps-an-open-source-solution-for-field-data</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DFLPL3/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DFLPL3/feedback/</feedback_url>
</event>
<event guid="2f74e20d-e852-5e90-a381-3d010fb89eb6" id="3297">
<room>SA01</room>
<title>NGV/survey: Offline data collection in 3D</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>[NGV/Survey3d](https://github.com/geoblocks/ngv/tree/master/src/apps/survey) is a customizable web application to collect data, in 3D, while offline.
Switch to offline mode, then:
- navigate the scene;
- see all defects, filter them;
- add or edit defects;
- fill in your form;
- load pictures;
- ... and synchronize to your backend when back online.
The application can be fully customized with:
- your collection areas;
- your 3d tilesets;
- your imagery;
- your form properties and its rules;
- your backend APIs;
- your authentication system.
Many tools are available:
- measures;
- coordinates in local projection;
- clipping polygons.
The solution is opensource, based on CesiumJS.
We are actively developing it with the support of our client: [HES](https://www.historicenvironment.scot/).</abstract>
<slug>foss4g-europe-2025-3297-ngv-survey-offline-data-collection-in-3d</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/7JCU8E/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/7JCU8E/feedback/</feedback_url>
</event>
<event guid="19e0813d-ea35-5781-8a89-8a9e0da58788" id="3175">
<room>SA01</room>
<title>QField: New Strategy and Application Potentials</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>With over 1 million downloads and 350,000 active users, QField is recognized as a digital public good that supports the UN Sustainable Development Goals.
Real-world use cases show how fieldworkers from all over the world are closing their data gaps to make qualified and informed decisions for the well-being of our livelihoods and for a sustainable future</abstract>
<slug>foss4g-europe-2025-3175-qfield-new-strategy-and-application-potentials</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/AKYST7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/AKYST7/feedback/</feedback_url>
</event>
<event guid="80b58cb7-ad5a-56a1-bbde-6d01e19d21be" id="3241">
<room>SA01</room>
<title>Open Source Solution for Topographic Data Production</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>National Land Survey of Finland has developed Topograhic Data Production System mainly based on Open Source solutions. Most important components are PostGIS, QGIS and QField. The new system will replace the current system step by step. The production is planned to start in April 2025 and it includes most of the topographic database themes.
This talk will discuss
- about the decision of using open source
- open source strategy
- the main features of the new solution
- experiences during the development
- experiences in the production
- future plans (e.g. generalization and cartographic products)</abstract>
<slug>foss4g-europe-2025-3241-open-source-solution-for-topographic-data-production</slug>
<track>Transition to FOSS4G</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/TZRN9K/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/TZRN9K/feedback/</feedback_url>
</event>
<event guid="d943da2f-cd53-567b-924d-696ecc1b098a" id="3485">
<room>SA01</room>
<title>Creating 3D Tiles</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>3D Tiles is an OGC community standard for streaming and rendering massive 3D geospatial data. This talk shows workflows to convert 3D formats like CityGML to 3D Tiles and compares
software tools like py3dtiles, mago 3DTiler and PLATEAU GIS Converter.</abstract>
<slug>foss4g-europe-2025-3485-creating-3d-tiles</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/GL39FY/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/GL39FY/feedback/</feedback_url>
</event>
<event guid="d5ebc88b-9c49-5e21-a9da-42fedf25e032" id="3407">
<room>SA01</room>
<title>Open source software for massive lidar data classification - The French LidarHD use case</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>As part of the national LiDAR HD program, IGN (French National Institute of Geographic and Forest Information) produces a 3D description of the French territory using LiDAR data, and distributes it in the form of point clouds and digital models.
The 3D point clouds captured as part of this program are first segmented into several classes (ground, water, vegetation, buildings, bridges, etc.), then transformed into DTM (digital terrain models), DSM (digital surface models) and DHM (digital height models).
To meet this challenge, IGN development teams have set up a processing chain based on Open-Source libraries (pdal, gdal, proj, etc.) and developed their own tools (A.I. classification, DTM and DSM derivation, thematic map calculation, water surface processing, etc.), most of which are Open-Source.
In detail:
* Myriad 3D: classification of point clouds using AI (https://github.com/IGNF/myria3d)
* Coclico: classification results comparison tool, agnostic of classification tools. (https://github.com/IGNF/coclico)
* CtView: calculation of thematic maps from point clouds (class maps with shading, density maps, etc.). (available soon)
* Lidro: treatment of watercourses in a lidar point cloud (to guarantee watercourse flow) (https://github.com/IGNF/lidro)
* GPAO : distribution and scheduling system for calculation (https://github.com/ign-gpao)
* ...
In this talk, we propose to first describe the whole process, then delve into the Open-Source software components we have designed and the uses of the OSGeo Open-Source libraries (pdal, gdal, proj, ...) we make in those tools.</abstract>
<slug>foss4g-europe-2025-3407-open-source-software-for-massive-lidar-data-classification-the-french-lidarhd-use-case</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/GLBELU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/GLBELU/feedback/</feedback_url>
</event>
<event guid="489177fc-9754-55fc-84f6-9c9c8ed68a65" id="3495">
<room>SA01</room>
<title>Structured File Storage vs. Millions of 3D Tiles: Who Wins?</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>When handling massive LiDAR datasets on the web, is the traditional gazillion-of-tiles approach still the best choice? Or can structured file storage like **SQLite**, **DuckDB**, and **Parquet** deliver a faster, more scalable format? Solutions for managing massive LiDAR datasets already exist, some even attempt to package **3D Tiles** into databases to reduce fragmentation. However, some of these approaches come with limitations: they are often proprietary or lack flexibility for large-scale datasets.
Faced with **1.3 trillion LiDAR points** from Croatia’s nationwide airborne LiDAR mapping project, we needed a way to efficiently store, process, and visualise this immense dataset. For web visualisation, Cesium and 3D Tiles offered powerful rendering, but converting raw LiDAR data into millions of tiny files led to a storage and performance nightmare. File fragmentation overwhelmed the filesystem, causing sluggish read/write operations, unreliable backups, and some request overhead when serving tiles online.
To overcome these challenges, we explored alternative structured file storage solutions.
SQLite, a compact embedded database, reduced fragmentation while enabling fast spatial queries. DuckDB, an analytical database optimized for large-scale data, delivered high-speed querying and processing power.
Parquet, a columnar storage format used in big data, provided strong compression and rapid sequential access, making it a promising alternative to millions of fragmented 3D Tiles.
In this presentation, we will share our experience, comparing fragmented 3D Tiles with structured file storage formats. Whether you’re working with 3D Tiles, small raster and vector tiles, or other massive spatial datasets, this session will provide you with insight to a practical alternative of the millions-of-files problem.</abstract>
<slug>foss4g-europe-2025-3495-structured-file-storage-vs-millions-of-3d-tiles-who-wins-</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/BXBM8A/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/BXBM8A/feedback/</feedback_url>
</event>
</room>
<room name="SA02" guid="65085993-f6f2-5613-95e2-cda29c59e1c5">
<event guid="c08bf289-2a49-5056-9cb3-7d7059e3150b" id="3389">
<room>SA02</room>
<title>GeoNode: Use Cases &amp; Custom Applications</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>GeoSolutions has been involved in several projects, ranging from local administrations to global institutions, involving GeoNode deployments, customizations and enhancements. A gallery of projects and use cases will showcase the versatility and effectiveness of GeoNode, both as a standalone application and as a service component, for building secured geodata catalogs and web mapping services, dashboards and geostories. In particular, the recent advancements. Examples of GeoNode’s builtin capabilities for extending and customizing its frontend application will also be showcased.</abstract>
<slug>foss4g-europe-2025-3389-geonode-use-cases-custom-applications</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/HHZMC9/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/HHZMC9/feedback/</feedback_url>
</event>
<event guid="d8c21bed-e3d5-5204-8c33-d43fef0c02e2" id="3481">
<room>SA02</room>
<title>GeoNetwork improvements for better usability</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>The NSDI Geoportal (National Spatial Data Infrastructure) serves as a starting point for viewing, searching, and downloading spatial data sources that are, according to the NSDI Act, part of National Spatial Data Infrastructure.
The main part of Geoportal is the metadata catalog, which was developed using the GeoNetwork catalog application to manage spatially referenced resources. It provides powerful metadata editing and search functions and is currently used in numerous Spatial Data Initiatives worldwide.
Implementing customized functionalities within the Geonetwork framework enabled an enhanced user experience, optimization of metadata search, and integration with other systems.
Key adaptations include the enhancement of spatial and attribute filters for better search results, an analytics module on the map component, customization of the metadata editor according to INSPIRE standards and NSDI specification for better interoperability, and enhancement of the administrative module for better user and group management.
GeoNetwork was integrated with Kibana dashboards, which provide an intuitive way of relaying data to the user, such as the number of defined metadata in the system, most searched keywords, the percentage of the metadata spatial scope, the number of searches, etc. Also, GeoNetwork was integrated with GeoHealthCheck, a Python application used to monitor the overall health (uptime and availability) of OGC services like WMS, WFS, WMTS, and ATOM.
The results of this work show that the customization of the GeoNetwork system within NSDI Geoportal significantly contributes to improving the accessibility of spatial data and increasing its usability for end users. The integration of customized functionalities enables more efficient searching, easier data analysis, and better interaction with other systems.
Keywords: NSDI Geoportal, GeoNetwork, interoperability, INSPIRE, spatial data, metadata</abstract>
<slug>foss4g-europe-2025-3481-geonetwork-improvements-for-better-usability</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/3WE9HN/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/3WE9HN/feedback/</feedback_url>
</event>
<event guid="d9d64359-01b9-52f4-945a-8cbd8bd15a2b" id="3291">
<room>SA02</room>
<title>Unearthing Proprietary Software into Open-Source: Lessons learned from TopoChronia, a QGIS Plugin for Reconstructing Digital Elevation Models of the Last 500 Million Years</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Reconstructing past topographies of the Earth is essential for understanding long-term interactions between the Earth’s interior, its surface and its atmosphere. We developed TopoChronia, an open-source QGIS plugin (https://github.com/florianfranz/topo_chronia) that generates Digital Elevation Models (DEMs) of the Earth's past (palaeo-DEMs), using plate tectonic reconstructions from the PANALESIS model. This represents a major transition from an outdated VBNET ArcGIS extension, which was developed in 2013 and is no longer functional due to software obsolescence.
The transition to open-source required a complete rewriting of the tool, involving both a platform shift from ArcGIS to QGIS and a language shift from VBNET to Python. One of the main challenges was ensuring the reproducibility of past results, as the old software could no longer be run, and documentation on the computational methodology was incomplete. This necessitated a careful reassessment of key processing steps, including the interpolation method used to construct DEMs from scattered elevation points. The previous tool relied on ArcGIS’s Natural Neighbor interpolation, which does not have a direct and reliable open-source equivalent. Through an in-depth comparative analysis, we determined that the Triangulated Irregular Network (TIN) interpolation in QGIS provided equivalent (and slightly improved) results in reconstructing topography and estimating sea level.
Beyond technical challenges, this transition also underscored broader issues in scientific reproducibility. Many methodological choices in past palaeogeographic reconstructions were undocumented or depended on expert knowledge not explicitly recorded in research papers. This lack of transparency complicated the validation of new results and highlighted the need for open software, clear documentation, and reproducible workflows in geospatial research.
In addition to software accessibility, data accessibility remains a critical issue. Until now, the standard practice for sharing the PANALESIS-derived palaeo-DEM datasets has relied on informal, manual distribution—researchers would need to request files by email. To address this limitation, we are developing a GeoServer-based solution to openly distribute palaeo-DEMs, aligning with FAIR (Findable, Accessible, Interoperable, and Reusable) principles. By making both the software and the data openly available, we aim to expand the use of deep-time palaeotopography in Earth system modeling and interdisciplinary research.
The development of TopoChronia demonstrates both the challenges and benefits of transitioning legacy geospatial tools to open-source frameworks. While the process required overcoming software obsolescence, re-evaluating computational methods, and addressing gaps in documentation, the result is a fully open, community-accessible, and sustainable tool that can be used and improved by researchers worldwide. By embracing open science principles, TopoChronia lays the foundation for more transparent and collaborative palaeogeographic research, ensuring that future studies can build upon reproducible and well-documented methodologies.</abstract>
<slug>foss4g-europe-2025-3291-unearthing-proprietary-software-into-open-source-lessons-learned-from-topochronia-a-qgis-plugin-for-reconstructing-digital-elevation-models-of-the-last-500-million-years</slug>
<track>Transition to FOSS4G</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/JACWAQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/JACWAQ/feedback/</feedback_url>
</event>
<event guid="1774cc2c-2964-5016-bc69-488d29849c8e" id="4003">
<room>SA02</room>
<title>Unifying open data of buildings through building a translator of taxonomies based on OpenStreetMap tagging format.</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>Geospatial data is essential for urban planning, disaster risk assessment, and infrastructure management. However, the independent nature of various taxonomies - such as the Global Earthquake Model (GEM), OASIS disaster risk framework, Building Stock Observatory (BSO), 3DCityDB for urban modeling, and Industry Foundation Classes (IFC) for BIM applications - creates barriers to interoperability. While these taxonomies provide structured, detailed datasets within their respective domains, their lack of standardization across platforms limits their usability in broader geospatial applications. OpenStreetMap (OSM), as a globally recognized open mapping platform, presents an opportunity to act as a baseline for integrating these diverse datasets. By aligning different taxonomies within an OSM-compatible framework, it becomes possible to enhance the comparability of structured geospatial datasets, ensuring that OSM data can be enriched and cross-referenced with external sources.
This work proposes an ontology-based approach to structuring and integrating multi-taxonomy building information within the OSM ecosystem. By defining mappings between attributes used in different taxonomies and translating them into standardized OSM tagging schemes, this approach allows for the seamless conversion of structured geospatial datasets into OSM-compatible formats. To integrate structured geospatial data into OSM, this work employs a multi-step methodology focusing on schema mapping, conversion pipelines, and tagging standardization. The first step in the process involves establishing correspondences between attributes across different taxonomies. Attributes such as building material, height, structural system, and occupancy classifications are identified in OSM and mapped to their equivalent definitions in GEM, OASIS, BSO, IFC, and 3DCityDB. For instance, a building tagged as `building:material=brick` in OSM corresponds to `wall_type=brick` in GEM, `Construction Material: Brick` in BSO, and `IfcMaterialDefinition=Brick` in IFC. This mapping ensures that structured datasets can be consistently translated into OSM-compatible key-value pairs, preserving the meaning of the original attributes.
Once these relationships are established, a structured conversion process is implemented to transform data into OSM’s key-value format. A transformation pipeline extracts structured geospatial attributes from external taxonomies, applies pre-defined mapping rules, and outputs the resulting dataset in an OSM-compatible tagging format. This step also involves data validation, where inconsistencies in classification are detected and adjusted to maintain uniformity. While there are cases of one-to-one transformation, there are also cases when that is not possible. For example, take the tag `MCF` which stands for material masonry, reinforcement confined. In these cases the nested information is transformed into two tags: `material=masonry;material:reinforcement=confined`. In case of later merging datasources, later there can be checked conflicting information, and priority in case of conflict is allowed. By employing a structured pipeline, batch processing of large datasets is possible, ensuring that structured geospatial information from various sources can be efficiently imported into OSM without requiring manual reclassification.
The integration of structured taxonomies into OSM is reinforced through the development of tagging presets for OSM editors. By generating JOSM XML and iD Editor JSON presets, contributors can apply predefined tags corresponding to structured taxonomies, reducing inconsistencies and improving data quality. These presets guide users in applying standardized geospatial attributes, ensuring that contributions align with structured datasets used in risk assessment, urban planning, and infrastructure monitoring.
The development of a multi-taxonomy integration model can have several advantages in structured geospatial data translation. By ensuring consistent mapping between GEM, OASIS, BSO, IFC, and 3DCityDB, the system enables conversion of structured data into OSM-compatible tagging formats. This enhances the accuracy of OSM mapping by aligning its tags with established geospatial standards. Furthermore, humanitarian organizations can directly leverage structured tagging presets to improve the consistency and reliability of OSM contributions. The use of structured presets reduces tagging inconsistencies and allows for direct comparisons between OSM-mapped infrastructure and standardized exposure models. The integration of structured datasets into OSM improves the platform’s usability for disaster resilience planning, enabling geospatial analysts to incorporate OSM data into machine-learning-based risk assessment models and structured exposure frameworks like GEM and OASIS.
The broader implications of this work extend to multiple fields. In disaster risk and exposure modeling, OSM can serve as a structured baseline for risk analysis in earthquake, flood, and climate resilience applications. In urban infrastructure monitoring, standardized tagging allows for better integration of OSM data with smart city models and BIM applications. Humanitarian mapping efforts can also benefit from this integration, as structured tagging ensures that OSM contributions align with professional risk assessment frameworks. The development of conversion pipelines and tagging presets ensures that structured datasets can be integrated into OSM without requiring manual intervention, significantly improving data comparability across platforms.
Future research will focus on expanding ontology coverage to additional geospatial datasets, including cadastral data and ISO standards. Further work will also explore the development of analytical tools to compare structured datasets with OSM data, enabling geospatial professionals to assess the completeness and accuracy of OSM-mapped building data. This initiative aligns with efforts to improve the interoperability of open geospatial data, making OSM an essential tool for humanitarian response, urban resilience, and infrastructure planning.</abstract>
<slug>foss4g-europe-2025-4003-unifying-open-data-of-buildings-through-building-a-translator-of-taxonomies-based-on-openstreetmap-tagging-format-</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>true</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/RXU77L/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/RXU77L/feedback/</feedback_url>
</event>
<event guid="9e9811a7-5c71-51b9-b079-916b64c07572" id="3218">
<room>SA02</room>
<title>VirtuGhan : A Virtual Computation Cube for EO Data</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>VirtuGhan is a Virtual Computation Cube designed for efficient on-the-fly tile computation of Earth Observation Data, similar to Google Earth Engine but utilizing open-source tools. It enables on-the-fly calculations on tiles on satellite images at various zoom levels using Cloud Optimized GeoTIFFs (COGs) &amp; rio-tiler. The project focuses efficient computation rather than storage, allowing direct analysis of images without needing to store the entire dataset. This approach enhances the accessibility and processing of large geospatial datasets. In this talk we will go through the current state of art on COG for the EO datasets , their capabilities , advatages of having on the fly tile computation on various zoom level and advantages of doing calculations on the tile. Read more here : https://github.com/kshitijrajsharma/VirtuGhan/</abstract>
<slug>foss4g-europe-2025-3218-virtughan-a-virtual-computation-cube-for-eo-data</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FQBPYC/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FQBPYC/feedback/</feedback_url>
</event>
<event guid="167bf127-05a5-5875-9379-e68492a0c4d9" id="3440">
<room>SA02</room>
<title>Big Data – Managing Large Volumes of Data from Diverse Sources</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>Handling "big data" (large volumes of data) primarily generated by automated weather stations and their installed sensors, requires special attention across the processes of data collection, processing/transformation, storage, presentation, and delivery.
The first step is identifying data sources and selecting appropriate access methods. Next comes data transformation, which depends on the data type and the relational database model (RDBMS) into which we plan to transfer it using adequate ETL tools. A critical aspect is ensuring the storage model accommodates heterogeneous sources while remaining efficient for access and delivery. The final step involves interfaces, either machine-based (API) or user-based (UI), that enable seamless data access and consumption.
Challenges increase significantly when dealing with spatial data, which comes in various forms, including numerical measurements, raster satellite imagery, radar images, and numerical forecasting models.
Each stage demands thorough planning and optimization to handle billion-record tables and terabytes of raster data effectively.
In this presentation, we will share our experiences in managing large datasets from diverse location and weather sensors, as well as spatial imagery, highlighting the complexities of processing and visualizing data.</abstract>
<slug>foss4g-europe-2025-3440-big-data-managing-large-volumes-of-data-from-diverse-sources</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/UA9CRS/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/UA9CRS/feedback/</feedback_url>
</event>
<event guid="03fe3055-af25-5770-ae27-2b31c93ec89d" id="3514">
<room>SA02</room>
<title>LLMs for geospatial data &amp; metadata</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>This presentation explores the innovative integration of Large Language Models (LLMs) into geospatial applications, focusing on vector data and location intelligence. Unlike traditional computer vision or imagery-based machine learning, this session delves into how LLMs can enhance the querying, analysis, and interpretation of geospatial tabular data.
1. Metadata Enrichment: Learn how LLMs can enhance metadata, improving the discoverability and usability of your geospatial datasets.
2. Natural Language Queries: Discover how LLMs can interpret and respond to natural language queries, making geospatial data more accessible to non-technical users.
3. Semantic Search: Understand how LLMs can elevate full-text search by comprehending the context and meaning of your queries, enabling more accurate and relevant results.
4. Intent Recognition: Explore how LLMs can discern user intent, whether it's displaying data on a map, searching for a dataset, or performing an analysis.
5. Reasoning and Function Calling: See how LLMs can leverage reasoning and function calling to address complex geospatial queries and tasks.
6. Conversational Interaction: Experience how LLMs can engage in conversational interactions, providing a more intuitive user experience.
7. Text-to-SQL Transformation: Witness how LLMs can convert natural language requests into precise data queries, including geospatial filtering and aggregation.
The demonstrations and examples in this presentation are built upon Free and Open Source geospatial solutions and adhere to OGC standards, ensuring interoperability and accessibility.
By the end of this presentation, attendees will gain a fresh perspective on the potential of LLMs in geospatial applications, with practical insights and real-world applications that can transform how you interact with and leverage your geospatial data.</abstract>
<slug>foss4g-europe-2025-3514-llms-for-geospatial-data-metadata</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/C9SGKH/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/C9SGKH/feedback/</feedback_url>
</event>
<event guid="7fa91005-064a-5fcf-a651-4af6101aa3e5" id="3457">
<room>SA02</room>
<title>fAIr - Community AI Assisted Mapping Roadmap</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) that aims to improve the efficiency and accuracy of mapping efforts for humanitarian purposes. The service uses AI models, specifically computer vision techniques, to detect objects in satellite and UAV imagery.</abstract>
<slug>foss4g-europe-2025-3457-fair-community-ai-assisted-mapping-roadmap</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/YFJFRK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/YFJFRK/feedback/</feedback_url>
</event>
<event guid="d7edf22d-7e96-575d-a237-01c3771c3944" id="3190">
<room>SA02</room>
<title>An Open Repository for LiDAR Data: FlaiHub and AI-Powered Analysis</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>The availability of LiDAR data worldwide is growing rapidly, with much of it being publicly accessible. However, fragmented datasets and accessibility challenges often hinder their usage. To address this, we are building an open repository of LiDAR data, consolidating diverse open datasets from various nations into a single, unified platform.
At the core of this initiative is FlaiHub, a tool that provides seamless access to these datasets while enabling data exploration and analysis. FlaiHub features an integrated viewer and AI-powered tools for both simple and advanced analyses, including point cloud classification and vectorization.
In this talk, we will explore the datasets currently available in the repository, demonstrate how you can contribute your data, and showcase how to use FlaiHub to view and analyze LiDAR data. Whether a researcher, professional, student, or enthusiast, you’ll learn how to leverage open LiDAR data and AI tools for your projects.
Join us to discover the possibilities of open LiDAR data and how you can be part of this growing community.</abstract>
<slug>foss4g-europe-2025-3190-an-open-repository-for-lidar-data-flaihub-and-ai-powered-analysis</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VNH8XU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VNH8XU/feedback/</feedback_url>
</event>
</room>
<room name="CA01" guid="d704391c-14ae-55a0-8038-46cac687da65">
<event guid="17a90c66-9a15-5d89-bd8c-daa5c5af9cc8" id="3295">
<room>CA01</room>
<title>OpenStreetMap-NG: Building a Better Map for Everyone</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>From its humble beginnings, OpenStreetMap has become a global resource. This talk takes a look at the ongoing development of OpenStreetMap-NG, an independent project focused on exploring the possibilities of modernizing the platform's infrastructure. I'll explain why these changes are being considered, explore the exciting possibilities they could unlock, and demonstrate how they might ultimately benefit users of the world's free map. This initiative is not affiliated with the OpenStreetMap Foundation.</abstract>
<slug>foss4g-europe-2025-3295-openstreetmap-ng-building-a-better-map-for-everyone</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/CP9GWS/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/CP9GWS/feedback/</feedback_url>
</event>
<event guid="7c0fd153-6616-5651-9d63-28f3f2a5f5a3" id="3487">
<room>CA01</room>
<title>Every building on Earth</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>The built environment is, globally speaking, the largest unknown in the understanding of the effects of disasters and in assessing their risk. Detailed knowledge of it is necessary for many tasks in disaster risk reduction but also in other fields, e.g. climate-related sustainability, urban planning and management, insurance and re-insurance. While in well-regulated countries cadastral data is available that provides various details about the buildings, in parts of the world such information is lacking and not even the locations of buildings and settlements are known to the authorities. Buildings, the core part of the built environment, can be strongly mixed within small areas in their structural types, sizes, shapes and number of people in them and the socio-economic structure can vary highly on these scales. This heterogeneity cannot adequately be described by classical exposure models that provide aggregated building data over larger areas.
A model that describes the built environment on the scale of each single building has never been accomplished, nor that such a model is dynamic, constantly updating with every change of the input data. Here, we present a high-resolution, open, and dynamic exposure model with the aim to provide global exposure data on the building level. This model is based on volunteered geographic information, predominantly OpenStreetMap and open data that is created with earth observation and machine learning, e.g. the building footprints of the Google Open Buildings and Microsoft ML Building Footprints, and the Global Human Settlement Layer to estimate the extent of built area. The different structural types of buildings per region are taken from open aggregated exposure models or developed from cadastral data. It covers every country and territory globally and is to a large degree building complete with approx. 2.7 billion buildings described in detail.</abstract>
<slug>foss4g-europe-2025-3487-every-building-on-earth</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/MQ3ZH9/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/MQ3ZH9/feedback/</feedback_url>
</event>
<event guid="42fb9acc-871d-5c92-aee7-b26feb44ced6" id="3479">
<room>CA01</room>
<title>Visualize, Navigate and Interact with 2D/3D geospatial data: State of iTowns</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>iTowns is an open-source, community-driven web framework designed for geospatial data visualization, navigation and interaction. It provides seamless 2D/3D rendering in a single, integrated package. Sponsored by the french National Institute of Geography (IGN) and Ciril Group, iTowns benefits from institutional and industry support to ensure long-term development and innovation.
Built with extensibility and interoperability in mind, iTowns out-of-the-box supports commonly-used OGC's open formats and protocols. This includes:
- fetching aerial photography and raw elevation data from WMS, WMTS and TMS servers
- stream in large 3D datasets: 3D Tiles, pointclouds; ...
- import various vectors formats: vector tiles, geoJSON, GPX, ...
- and those requested by the community!
The iTowns team would like to share the recent technical developments and future roadmap of iTowns and present its ecosystem. We have on-going work for a major 3.0 release, aimed at offering a serious alternative to proprietary geospatial solutions. Thanks to NGI NLNet founding, we hope to offer support for a street-view alternative using Panoramax datasets, WebGPU acceleration and AR/VR for popular headsets.
The project's roadmap is openly accessible and can be adjusted collaboratively, allowing sponsors to initiate top-down changes and users/contributors to propose bottom-up modifications. This dual mechanism ensures a dynamic and inclusive process for roadmap updates. Currently, iTowns has a strong French community, with the addition of a few European contributors. With the upcoming 3.0 release and this conference, we hope to convince more players across Europe to join the project, fostering a more diverse and collaborative open-source geospatial ecosystem.</abstract>
<slug>foss4g-europe-2025-3479-visualize-navigate-and-interact-with-2d-3d-geospatial-data-state-of-itowns</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/G7JGXK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/G7JGXK/feedback/</feedback_url>
</event>
<event guid="8bda5c56-b59c-5f8b-a0bf-f66d24b795b3" id="3488">
<room>CA01</room>
<title>GeoNetwork 5 Status Report</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>GeoNetwork is the most successful catalog application in Europe, used by more national governments than any other solution. With such a cornerstone technology it is important to ensure it remains sustainable, secure, and available.
GeoNetwork 5 is a bold reimagining of the project, providing a "store" of components that can be configured for use. This approach takes advantage of the Spring Boot "App" architecture for a fresh start targeting the Java 21 environment. There is also a new user interface approach to share, making use of TypeScript Angular components.
Together these choices open up some exciting opportunities, allowing creativity in catalog design and presentation, and scaling services independently. This flexibility is also important strategically allowing GeoNetwork 5 to migrate modules one at a time, while delegating to GeoNetwork 4 implementation during development. We can also look at new developments taking place within this architecture, with progress on OGCAPI-Records, and DCAT2.
This work is ongoing and we will look at how the project is supported by organizations around the world, and opportunities to look forward to as the project progresses.</abstract>
<slug>foss4g-europe-2025-3488-geonetwork-5-status-report</slug>
<track>FOSS4G ‘Made in Europe’</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/SJWFAK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/SJWFAK/feedback/</feedback_url>
</event>
<event guid="60c33d74-1a24-55f6-8a88-58d06a0f1791" id="3513">
<room>CA01</room>
<title>Geospatial ES|QL in Elasticsearch</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>Elasticsearch has long offered powerful geospatial capabilities, yet it remains underutilized by the open-source GIS community. Two major developments in the past year are changing this. First, Elasticsearch returned to an approved open-source license, reigniting interest among developers. More significantly, the introduction of ES|QL, a declarative query language, has paved the way for OGC-like geospatial functions. This shift makes Elasticsearch feel more familiar to users of tools like PostGIS.
In this talk, we’ll explore the current capabilities of Geospatial ES|QL, demonstrate real-world geospatial search and analytics, and provide a glimpse into future developments that will further enhance Elasticsearch as a geospatial database.</abstract>
<slug>foss4g-europe-2025-3513-geospatial-es-ql-in-elasticsearch</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FWTGAZ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FWTGAZ/feedback/</feedback_url>
</event>
<event guid="d2c0d556-0377-504e-afb1-ef11f2b853cf" id="3345">
<room>CA01</room>
<title>Visualizing Queries: A User-Friendly CQL2 Filter Builder for Geospatial Data</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>CQL2 (Common Query Language2 - https://www.ogc.org/publications/standard/cql2/) unlocks powerful filtering for geospatial data, but writing queries by hand can be daunting. This talk presents a TypeScript-based CQL2 parser and a visual filter builder that make query creation intuitive and developer-friendly. We’ll explore how the parser enables transformation, serialization and validation of CQL2, and how the UI simplifies query building with real-time previews and a no-code approach.
I'll show how this combination improves both developer workflows and user experience, making CQL2 more accessible for everyone. Whether you're coding queries or crafting filters visually, this session will help you get the most out of CQL2.</abstract>
<slug>foss4g-europe-2025-3345-visualizing-queries-a-user-friendly-cql2-filter-builder-for-geospatial-data</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/MMAN7C/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/MMAN7C/feedback/</feedback_url>
</event>
<event guid="a64ae7ec-292b-54db-8e5c-8bfbed4cbfd7" id="3379">
<room>CA01</room>
<title>GeoShop: Ordering and delivery tool for geospatial data</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Abstract:
In this presentation, we introduce GeoShop – an open-source, e-commerce-like application that we use in projects to facilitate the ordering of geospatial data from GIS systems. Using real-world examples, we will demonstrate how this Django-based application can be used to handle both free of fee and chargeable data orders from various geospatial data infrastructures. Additionally, we present Extract, another open-source solution designed to orchestrate data extractions from data providers. Extract is necessary to the correct working of GeoShop.
Description:
GeoShop is an e-commerce-like application that enables the ordering of geospatial data from a GIS system. In the present context, it is used with various WebGIS services. However, GeoShop can also be integrated with other applications that do not necessarily include a map viewer.
GeoShop allows the offering and sale of diverse geospatial data packages in various formats, such as vector data, 2D and 3D datasets, plans, maps, and aerial images – all just a few clicks away and available within minutes. Open data products can be provided free of charge. Furthermore, data can be supplied according to different pricing models.
While GeoShop manages the ordering process, the actual data extraction is handled by Extract, an open-source Java-based solution. Extract listens to the orders placed on GeoShop and executes data extractions using GDAL scripts or other custom extraction processes. The extracted data is then uploaded back to GeoShop, ready for download by the user.
This architecture enables multiple data providers to connect to the same GeoShop instance while maintaining full control over their data and extraction processes. Each provider can manage their own extraction scripts and infrastructure, ensuring flexibility and security. The ordering process of geospatial data is automated, therefore minimizing the need for manual data handling, except in highly specific cases.
During the development phase, significant resources were invested in optimizing performance and user experience, thanks to the initiative of the Geoinformatics Service of the Canton of Neuchâtel, Switzerland. They donated the code as an open-source project to Camptocamp SA for further development.</abstract>
<slug>foss4g-europe-2025-3379-geoshop-ordering-and-delivery-tool-for-geospatial-data</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/PTPALW/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/PTPALW/feedback/</feedback_url>
</event>
<event guid="24502e59-6fd5-5dc4-87d0-1b2c41bfae81" id="3438">
<room>CA01</room>
<title>Serverless Rasters on the Web: The MapLibre COG Protocol extension</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>In recent years, the approach to loading vector data in web browsers has shifted from traditional WMS/OGC methods to vector tiles, allowing for more efficient rendering and processing. With increasing bandwidth and more powerful CPU/GPU capabilities, handling data directly in the browser has become the preferred choice. The same evolution should apply to raster data: instead of preprocessing it on the server, a more efficient alternative is to load it directly as a Cloud-Optimized GeoTIFF (COG).
The MapLibre COG Protocol extension is a powerful tool for visualizing COGs directly in MapLibre GL JS. By leveraging HTTP range requests, it efficiently loads only the necessary portions of large raster datasets, reducing bandwidth usage and improving performance. This makes it ideal for handling high-resolution satellite imagery, elevation models, and other geospatial data. Since it enables direct cloud access from MapLibre, organizations can serve large datasets without expensive infrastructure, making it a cost-effective and scalable solution.
Additionally, the library supports dynamic adjustments, such as color manipulation and band mixing directly on the browser, providing greater flexibility for dynamic data visualization. As an open-source, community-driven project, it aligns with other open geospatial tools, standards and practices ensuring adaptability to future scenarios.
By combining efficiency, scalability, and ease of integration, the MapLibre COG Protocol simplifies the use of COGs in web mapping applications, making it an essential tool for developers, researchers, and GIS professionals working with cloud-based geospatial data.
Custom protocol to load Cloud Optimized GeoTIFFs (COG) in Maplibre GL JS
Github: https://github.com/geomatico/maplibre-cog-protocol
NPM: https://www.npmjs.com/package/@geomatico/maplibre-cog-protocol</abstract>
<slug>foss4g-europe-2025-3438-serverless-rasters-on-the-web-the-maplibre-cog-protocol-extension</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/989LUY/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/989LUY/feedback/</feedback_url>
</event>
<event guid="cdb326ef-fcac-5e7c-99ed-2c97cad05a47" id="3317">
<room>CA01</room>
<title>Superset - Business Intelligence meets Cartography</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>Superset is one of the most used open source tools for business intelligence (BI) and currently the most starred Github project of the Apache Software Foundation. We extended Superset with support for thematic maps that allows for the analysis of spatio-temporal processes. In this talk, we will demonstrate how cartodiagrams, choropleth maps, as well as proportional symbol maps can be created in Superset and how they contribute to an advanced analysis of spatio-temporal processes. We will also talk about future plans for additional geospatial features in Superset, such as geospatial filters and data sources.</abstract>
<slug>foss4g-europe-2025-3317-superset-business-intelligence-meets-cartography</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/KFFETQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/KFFETQ/feedback/</feedback_url>
</event>
</room>
<room name="PA01" guid="16fc454c-4a03-597f-99ce-1866d0ded964">
<event guid="234ca19f-f786-59ad-b189-05bcb28e38ed" id="3981">
<room>PA01</room>
<title>Exploratory analysis of beta diversity across altitude gradients in an Alpine region (Trentino) using FOSS4G and a historical floristic archive</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>INTRODUCTION
Biodiversity is a crucial yet complex concept in ecological research. Beta diversity, representing species turnover across spatial gradients, plays a key role in understanding ecosystem functioning and conservation planning. Studies suggest that environmental factors such as altitude, latitude, and geographical distance drive beta diversity patterns. However, large-scale analyses may not directly inform local conservation efforts. Therefore, fine-scale assessments within specific administrative regions are essential for effective biodiversity management and protected area planning. In FORCING project (Geri et al.2016), the Edmund Mach Foundation and the University of Trento recovered a huge database of vegetation surveys, build in the 1970s to represent the "Schmid's vegetation belts" in the forests of Province of Trento a region of about 6.212 km² in the northeastern Italian Alps with a huge flora and fauna biodiversity (Tattoni et al. 2021). The surveys and the cartographic materials were digitized and organized in a geographic geodatabase with QGIS and postGIS. The sampling design of this archive lends itself perfectly to being analyzed from a beta diversity perspective, permitting to compare several environmental gradients in terms of species turnover and species richness. The archive was created using FOSS4G and is permanently available and stored in a web-GIS hosted on servers maintained by Fondazione Edmund Mach and is accessible at http://meteogis.fmach.it/forcing/ (unfortunately due to a technical problem related to an ongoing general software update the access maybe unavailable until the end of 2025).
The aim of this paper is to test the use of FOSS4G software (Ciolli et al. 2017) and the FORCING geodatabase (Geri et al.2016) to perform an exploratory analysis of the floristic species turnover in a relative small area, and to try to underline some patterns and driving forces.
METHODS
The data coming from the original sampling project were managed and prepared using Qgis software, using the Spatialite format geographic database and georeferenced in the WGS84 UTM 32N coordinate reference system (srid: 32632). 517 linear transects and a total of 190761 species records were analysed (Geri et al. 2016). The statistical analysis were performed with R software. In each linear transect, considered a single ecological community, the beta diversity using site as simple point were calculated evaluating in this way the degree of species turnover across the environmental gradient that is created along the transect (Tuomisto, 2010). The basic statistical properties extracted for each transect were put in relation with the beta diversity index and with the corresponding values of species richness, producing graphs that shows the various relations trend. The significance of the linear relations were tested using the Pearson correlation coefficient. Each belt was compared in terms of species composition using the Sørensen’s coefficient of similarity. The behavior of the beta diversity and species richness were deepened in terms of variance partitioning. It was tested the variance explained by the four variables: mean altitude, mean slope, range of altitude and range of slope against beta diversity and species richness. The analysis should stress the role of the variable in single or in multiple way to drive the species turnover. The variance partitioning analysis were processed using the Vegan library of the R statistical software (R Core Team, 2024), and in particular the module “varpart”. This function partitions the variation of response data table with respect to two, three, or four explanatory tables, using redundancy analysis ordination (RDA). To simplify the results interpretation the variance partitioning in combinations of group of three variables was applied. Both terrain altitude and slope data and both vegetation beta diversity and species richness data were transformed with a log transformation in order to obtain a normal distribution of data. QGIS was used also for data exploration and representation.
RESULTS
Pearson indices show that all the variables are significative except the slope variance for both beta diversity and species richness and the mean altitude only for species richness. Generally both species richness and beta diversity grow increasing altitudinal range, slope range and slope mean while variance doesn’t show a definite trend considering in particular way the species richness. Sorensen statistic shows how the similarity decreases with increasing the altitude separation from the lower level, and highlights the pairwise comparison between altitude adjacent belts. The latter statistic shows how the similarity presents a different behavior with the increase of altitude, rising very fast in the first step (between 0 and 600 meters) then leveled off and finally decrease in correspondence to the last two steps, between 1500 meters to 2100 meters. The variable that explain much more variance is the altitude variance for both beta diversity and species richness. Regarding beta diversity the greater joined effect is due to the combination of altitude range and slope mean while altitude range, slope mean and slope range presents an higher joined effect for three variables.
DISCUSSIONS AND CONCLUSIONS
The results confirm that the transects characterized by a wider range of slope and elevations show a higher rate of beta diversity. This is reasonable, since the more are the different environments the transects cross, the more pronounced should be beta diversity. This is also confirmed by the linear relation of the single variables highlighted both as graphical trend and by the pearson tests and moreover, by the fact that the variance is explained as a joint action of variables.
Finally this work confirmed that FOSS4G software is perfectly suitable to be used to perform spatial statistical analysis to study beta diversity both from the point of view of numerical statistic and from the point of view of geostatistics (Ciolli et al. 2017) showcasing the power and versatility of these tools.
Further future developments and analysis will include the comparison of beta diversity of the present vegetation with other historical floristic archives sampling (Lelli et al 2023), statistical analysis of the data using different set of statistical and geostatistical techniques and finally to include remote sensed data.</abstract>
<slug>foss4g-europe-2025-3981-exploratory-analysis-of-beta-diversity-across-altitude-gradients-in-an-alpine-region-trentino-using-foss4g-and-a-historical-floristic-archive</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/TEDKSY/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/TEDKSY/feedback/</feedback_url>
</event>
<event guid="fd6cbf28-5e55-525c-a0f2-175aeb28bac3" id="3984">
<room>PA01</room>
<title>OpenTrack: a Sensor for Monitoring the Usage of Territory</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>Monitoring the movement of people, animals, and vehicles in daily territorial use can significantly improve spatial development, enhancing safety, sustainability, and inclusiveness. Across Ticino canton, many stakeholders, such as regional natural parks, are eager for a system that can provide valuable data on space usage to better manage costs, justify new investments, and handle maintenance activities [1, 2].
During the INSUBRIPARKS Interreg project, a cost-effective prototype was developed to track and count the passage of tourists in specific park areas. The system consists of a device with a camera that, through image recognition and machine learning techniques, collects data that are then sent to a data warehouse based on istSOS [3], an open-source implementation of the Sensor Observation Service (SOS) standard of the Open Geospatial Consortium (OGC). The system fully complies with European GDPR regulations, as it only stores anonymous metadata such as the type of object (person, car, bicycle, etc.) and the object's movement path. No video or images captured by the camera are saved.
Thanks to these features, the device has been adopted also in the Adaptive Space project, funded by the Federal Office of the Spatial Development (ARE). This project aims to develop a protocol with guidelines for the inclusive planning of last-mile mobility.
To this end, two sites were selected as study areas (SA) to analyse the behaviour of citizens who frequently use these spaces. One is located outside the Mendrisio railway station (SA1). This area is occupied by four parking lots and is subject to movements that prioritize pedestrian passage and vehicle flow to and from the station and the city centre. The second site is located at Mendrisio S. Martino, also outside the railway station (SA2). This area is of particular interest because new structures have been built over the past year, impacting pedestrian use due to an increase in traffic from both vehicles and people. In fact, this area is commonly used as a passageway for people heading to the industrial zone.
The methodology involved an automatic detection approach by installing sensors to collect continuous data. Three main data collection campaigns were conducted at each site: one in summer, one in autumn, and one in winter. Since the device has high power consumption, it had to be installed with a battery, as no viable solution was found to connect the sensor to a continuous power source. During the campaigns, the device collected data on the number of detected objects, their classification, and their movements across the monitored areas, using tracking capabilities that gather coordinates frame by frame to monitor the movement of each object. Such data have been validated through manual sampling and, on the other hand, have been provided a broader overview of the usage of the selected areas across different periods of the year.
The analysis developed during this project focused on tracking data coordinates, which proved to be essential for understanding how the objects are distributed across the area and determining where activities are most concentrated, based on the different categories to which each object belongs. This approach results in the generation of heatmaps for pedestrians and vehicles using data from the entire day, as well as filtering for evening and morning peak-hour traffic. The dataset has also been evaluated in terms of data accuracy, as for each object present in the frame, the percent of confidence is archived. By plotting this data through a histogram, it was possible to understand the accuracy assessments of the detected objects from the chosen classification model.
Furthermore, two different analytical methods were applied to the two study areas. In SA1, alongside heatmap generation and accuracy evaluation, the analysis focused on parking areas by calculating the stationary time of detected objects, which helped to assess how these parking areas are utilized by citizens. In contrast, in SA2, a different approach was taken, custom-defined zones were created to analyze object counts and determine the percentage of people or vehicles using specific parts of the area compared to the rest.
In this context, the challenges encountered during the project will be reported, primarily those related to data transmission. Due to the large amount of data collected, it was difficult to transmit everything using only an NB-IoT connection via the MQTT standard, which, due to its low bandwidth, cannot handle the transmission of large amounts of data.
Thanks to this research, new advancements have been made using this device firstly developed during the INSUBRIPARKS project, such as analysis based on object tracking coordinates rather than solely relying on object counts. However, further developments are needed, including the possibility of georeferencing the data, since the current system uses an absolute reference system based on image coordinates, and improving the overall performance of the device. One of the critical aspects in this regard is the video streaming frame rate, which currently ranges from 15 to 19 FPS. A more powerful device, combined with a higher-resolution camera, could achieve 30–40 FPS, which would enhance both detection accuracy and the ability to track object positions more precisely during video capture.
In conclusion, this paper presents and analyses the collected data, along with the preliminary results derived from the implemented methodology, where tracking data served as the raw input for all analyses. This approach is highly promising in providing valuable insights for urban planners to improve the studied areas, enhancing security, and supporting sustainable and inclusive urban development.</abstract>
<slug>foss4g-europe-2025-3984-opentrack-a-sensor-for-monitoring-the-usage-of-territory</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8NPSRZ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8NPSRZ/feedback/</feedback_url>
</event>
<event guid="29da03ee-72bc-5fd0-b3c2-6f701bdc6a39" id="3971">
<room>PA01</room>
<title>Evaluation of Spatial Interpolation Methods for Wind Speed and Direction: A Case Study in Split-Dalmatia County</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Wind is a natural movement of air caused by variations in air pressure due to the uneven heating of the Earth. Wind speed and direction are dynamic variables that fluctuate over both time and space. These variables are crucial for urban and spatial planning, agriculture and crop management, sports event organization, aerial navigation, air pollution modeling, and fire management. The latter is especially important, as fire behavior and spread are significantly influenced by wind conditions at the exact location. Thus, accurately determining wind conditions at a given location is essential. Typically, wind measurement is performed at sparse locations using weather instruments. Although these measurements are conducted according to strict standards—typically at an altitude of 10 meters above ground, on grassy terrain, and without nearby obstacles—capturing wind speed and direction at a single point in space and time does not fully represent the broader conditions. Thus, various spatial methods are utilized for wind interpolation. Wind interpolation refers to the process of estimating wind speed and direction at locations where no direct measurements are available.
This paper investigates the effectiveness of selected interpolation methods for estimating wind speed and direction at unknown locations, using measurements from a network of weather stations. Four well-established methods were considered: Natural Neighbor (NN)[1], Inverse Distance Weighting (IDW), Kriging (K), and Ordinary Kriging (OK)[2]. The study focuses on the Split-Dalmatia County region.
METHODOLOGY
For this purpose, wind measurement data from 28 weather stations with continuous data availability was utilized. Data from weather stations distributed across Split-Dalmatia County were collected throughout 2024 from the Weather Underground website[3]. This service integrates meteorological data from both public and privately owned weather stations. The data was preprocessed, and scenarios representing simultaneous measurements were selected and included in the analysis. These scenarios corresponded to three main wind directions (Bora, Sirocco, and Mistral), four seasons (Winter, Spring, Summer, and Autumn), and different times of day (morning, afternoon, and evening). Since some wind directions are uncommon in certain seasons or times of the day, a total of 28 unique scenarios were used in this study.
Of the 28 stations, data from 24 stations was used for wind interpolation across the study area, while four were selected as "unknown" locations for comparison with the interpolated values. In two experiments, the four unknown stations were chosen to represent: (1) locations with distinct geographical challenges (land, coast, canyon, and island locations) and (2) a station spatially surrounded by known measurements. For each experiment, scenario, and interpolation method, we calculated and analyzed the Root Mean Squared Error (RMSE), Mean Absolute Error in the zonal u-direction (MAE u), and Mean Absolute Error in the meridional v-direction (MAE v) for the unknown stations, using actual measurements as the ground truth and comparing them with interpolated values.
Interpolation was performed using the Python packages Rasterio, PyKrige, and Delaunay, as well as some custom code, while the visualization of interpolated values was conducted using QGIS software. The analysis was carried out in Python, utilizing the Seaborn and Matplotlib libraries to generate a series of charts that revealed noteworthy findings.
RESULTS
The evaluation of interpolation methods demonstrated that Ordinary Kriging achieved the lowest interpolation errors, likely due to its ability to account for spatial autocorrelation and incorporate data from multiple nearby stations. Despite utilizing a significant number of measurements, distance-based weighting methods, such as IDW and Natural Neighbors, exhibited higher errors, with RMSE values reaching up to 5 m/s. This highlights the impact of terrain complexity on accurate wind interpolation.
When analyzing the median values of exhibited errors, IDW methods showed the lowest RMSE vector and MAE v (meridional) direction, while Ordinary Kriging produced the lowest median MAE in the u (zonal) direction.
Spatial analysis revealed that interpolation accuracy varied significantly by location, with the station situated in a river canyon displaying the highest errors across all methods. This underscores the difficulty of wind interpolation in complex terrains. Wind type also played a crucial role, with Bora producing the highest RMSE values (up to 8 m/s) across all methods due to its turbulent nature. In contrast, Jugo and Maestral, with their steadier patterns, resulted in lower errors (below 3 m/s).
A separate analysis of the u (zonal) and v (meridional) wind components indicated no significant difference in interpolation accuracy between them, as both components contribute equally to wind variability. However, certain locations exhibited elevated errors during Maestral wind scenarios. Upon closer examination, this can be attributed to their positioning relative to the coastline and surrounding topography.
A comparison of errors between both wind components showed that Maestral exhibited significant errors in eastward-oriented directions, affecting both rural and urban areas.
Visualization of interpolation across the entire study area revealed that the examined methods struggled to adapt to local conditions. While Kriging produced wind field maps with expected variations in wind speed and direction, it statistically resulted in less accurate predictions. However, maps resulting from other methods do not exhibit expected spatial patterns of the wind field.
CONCLUSIONS
This study underscores the critical role of terrain complexity, wind type, and station placement in determining interpolation accuracy, particularly in challenging environments such as canyons, where conventional methods struggle to capture abrupt wind variations.
Future research on wind interpolation should focus on integrating high-resolution topographic and land cover data to improve accuracy, especially in complex terrains. Machine learning techniques, utilizing historical data, could enhance predictive capabilities by capturing intricate spatial patterns. Expanding the study to include time-series analysis and temporal interpolation would provide better wind forecasting insights. Additionally, leveraging higher-resolution datasets from remote sensing and hybrid approaches that combine statistical and physics-based models could refine wind field predictions. Validating these methods across diverse geographic regions and developing real-time applications for fire management and disaster response would further enhance the practical utility of wind interpolation techniques.</abstract>
<slug>foss4g-europe-2025-3971-evaluation-of-spatial-interpolation-methods-for-wind-speed-and-direction-a-case-study-in-split-dalmatia-county</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/JNM3AC/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/JNM3AC/feedback/</feedback_url>
</event>
<event guid="d4ed6201-7df6-5b4e-ae3e-6b7e463c435c" id="3983">
<room>PA01</room>
<title>A streamlined GIS interface for Citizen Science activities: QGIS Light</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>Citizen science has become a powerful approach for engaging the public in scientific research, particularly in environmental monitoring [1]. Activities such as tracking air quality, mapping biodiversity, and assessing water quality benefit significantly from citizens going outdoors to collect data. In this regard, geospatial tools are essential for ensuring precise and efficient data collection [2]. Beyond data collection, geospatial tools also enable effective data visualization and exploratory data analysis, allowing users to overlay different datasets, revealing spatial relationships and trends that may not be immediately apparent. This analytical capability empowers citizens to help generate meaningful insights and support evidence-based policies together with researchers and policymakers [3].
However, one of the main challenges in such activities is making spatial analysis functions easily accessible to non-experts. Many GIS software programs offer powerful features that can support citizen science by allowing users to explore real-world datasets and analyze spatial relationships. But, non-technical users with limited exposure to geospatial software often struggle with complex interfaces and the technical nature of these tools. This is especially the case for elders and young students, who may have limited data literacy or experience with data analysis methods. Tasks like accessing and managing datasets, performing spatial analyses, and visualizing results typically require specialized training and guidance due to the steep learning curves of default user interfaces. Case studies have demonstrated that more accessible interfaces can enable previously underserved groups to participate more actively in citizen science activities [4]. Therefore, developing intuitive and simple interfaces for core GIS functions can lower barriers to spatial data exploration and increase participation from such groups in geo-citizen science initiatives.
To reduce this barrier, we developed a simplified user interface for QGIS, the most widely used free and open GIS software, tailored to the specific needs of non-technical users with a focus on citizen science. To achieve this, we began by evaluating all QGIS components and features in detail, considering both functionality and complexity. Based on this assessment, non-essential and duplicate components were removed, and complex features were replaced with simpler alternatives. We also simplified data visualization and processing functionalities to hide technical complexities. Advanced feature groups such as SQL, Z/M, TIN, mesh, tile, curve, GPS, GRASS, and PDAL were hidden from the user. The remaining essential components were reorganized to streamline typical workflows and improve access to frequently used features. The processing toolbox and menus were removed, and all necessary items were made available as tool buttons. The number of toolbars was reduced to two - one for core functions and another for editing. Common functions, such as zooming and selecting, were grouped and made available through dropdown tool buttons to create a compact yet efficient interface. Only the overview and layer panels are made visible by default, with others appearing only when needed. The locations of all components are fixed to ensure a consistent user experience, particularly during training for non-technical users. Lastly, additional features were added to enhance the user experience. Plot functions were replaced with DataPlotly, allowing users to easily change plotting options and access plots directly. Common base maps were made available through QuickMapServices, providing a wide variety of base maps that can be added as layers effortlessly.
To provide access to the streamlined user interface and allow users to easily switch between interfaces, QGIS Light was developed as a QGIS plug-in. The plug-in utilizes customization options available in QGIS and, when necessary, interacts directly with the user interface framework (Qt) to enable advanced customizations not natively supported by QGIS. Users can easily adjust the applied simplifications by editing a configuration file to disable specific simplifications or enable new ones. The plug-in is available on the QGIS plug-in repository, making it easy to install through the QGIS plug-in manager. The source code is open access under the GPL 3.0 license [5], and the open-source code repository is hosted on GitHub to facilitate collaboration (https://github.com/ITC-CRIB/qgis-light).
In this paper, we share our experience in implementing a practical and intuitive user interface for a powerful FOSS GIS application (QGIS) that can be learned and used quickly without prior technical knowledge. We discuss the design process aimed at simplifying the user interface by describing the user stories that guided this effort (e.g., "As a user, I want to work with a single map at a time") and explain how these stories were translated into user interface modifications (e.g., disabling multiple map views). Additionally, we share the details of the usability assessment study of a long list of QGIS components and features. During this study, we identified several issues that hindered a better user experience, such as inconsistent terminology, similar tools with different parameter sets, tools with almost identical names performing different tasks, and tools that could be integrated into others. Our findings suggest that critically reviewing existing user interface elements and streamlining them into a more refined and standardized experience could improve usability in QGIS. This approach could also inform simplification efforts in other GIS software. The paper will also present the details of these identified issues.
In conclusion, the primary goal of QGIS Light is to provide a simplified entry point for non-technical individuals to engage in spatial data analysis. While non-technical people are often involved in data collection part of citizen science activities through specialized applications, they are typically excluded from data analysis and evaluation, which is usually handled by experts. By offering a simplified GIS interface, we can make spatial analysis more accessible to such people without sacrificing the software's powerful capabilities. This approach can serve as a stepping stone, allowing users to gradually transition to the standard GIS interface and advanced features, fostering GIS community growth besides participation in citizen science activities. In fact, a simple interface might be useful for anybody that requires core data visualization, editing, and analysis functionality, and can facilitate education, capacity development, and even professional activities.</abstract>
<slug>foss4g-europe-2025-3983-a-streamlined-gis-interface-for-citizen-science-activities-qgis-light</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VWZ8A7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VWZ8A7/feedback/</feedback_url>
</event>
<event guid="bf1b73fa-1114-565f-8fab-aedb25a34172" id="3966">
<room>PA01</room>
<title>Modeling ecosystem services in Armenia using InVEST: a scenario-based approach with NextGIS Web integration for public awareness and engagement</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>This study explores the application of the open-source InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) [1] tool to model ecosystem services in Armenia, utilizing a scenario-based approach. By simulating two hypothetical scenarios, where all natural terrestrial land cover classes are replaced with bare ground or croplands, the study emphasizes the critical role of terrestrial ecosystems in ecosystem service provisioning. The results are published through Web GIS platforms powered by open-source framework NextGIS Web (https://github.com/nextgis/nextgisweb), providing an interactive medium for engaging civil society and fostering public awareness. This integration of advanced modeling techniques with accessible web-based dissemination aims to influence strategic policy-making in forest management, water resource allocation, and urban planning. The findings highlight the potential of scenario analysis and Web GIS to support sustainable development by illustrating the value of ecosystem services to both policymakers and the public.
Study scope
Ecosystem services, the benefits humans derive from nature, are crucial for supporting human well-being [2]. In the context of Armenia, a country with diverse landscapes and significant environmental challenges, understanding and sustainable managing these services is vital. The widely discussed bill in Armenia since the beginning of 2025, "On the Launch of the Process of Accession of the Republic of Armenia to the European Union", increases the need to raise public awareness about issues of sustainable use of ecosystems, maintaining ecosystem services and biodiversity protection
This study employs the InVEST tool, an open-source software suite designed for ecosystem service modeling, to assess critical services on example of Sediment Delivery Ratio, Seasonal Water Yield, Urban Cooling, and Urban Flood Risk Mitigation models. By adopting a scenario-based approach, we aim to estimate the physical volume of ES provided by natural ecosystems and changes in it from 2017 and 2023. Our approach involves several key steps: data collection, scenario development, models parametrization, statistics calculations over model outcomes, mapping and results publishing via Web GIS.
Materials and methods
First, we collected geospatial data relevant to the four ecosystem services, including land cover, relief, soil properties, climate data, and urban infrastructure. We used only global public domain datasets and public domain Armenian sources to increase study transparency and reproductivity. Source datasets were transformed to meet different scenarios conditions, for example land cover dataset was recalculated to scenarios “all natural vegetation turns to bare land” and “all terrestrial land cover classes except built-up areas turn to cropland”.
Then, we calibrated four InVEST models to reflect the specific conditions of Armenia, ensuring accurate simulations of each service under different scenarios. Selected InVEST models:
1. Sediment Delivery Ratio (SDR) model evaluates how well a land area can prevent sediment from being eroded and transported, based on factors like terrain, climate, vegetation, and land management practices
2. Seasonal Water Yield model calculates the amount of water generated by a watershed and delivered to streams. Its main outputs are quickflow, local recharge, and baseflow. Quickflow measures rainfall that flows over the land surface immediately or shortly after rain. Local recharge quantifies water that infiltrates the soil, minus what is lost to evaporation or vegetation use. Baseflow accounts for water reaching streams more slowly via underground pathways, including during dry periods. The model relies on inputs such as elevation, soil properties, land cover, rainfall patterns.
3. Urban Cooling model evaluates heat mitigation by calculating an index based on factors like shade, evapotranspiration, surface reflectivity (albedo), and proximity to cooling areas such as parks.
4. Urban Flood Risk Mitigation model estimates the reduction in runoff, or the volume of stormwater retained at each pixel relative to the total storm volume, based on land cover and soil properties.
Outcomes of all models under different scenarios were mapped and published as web maps. Also, statistics for the most significant outputs were calculated for each province and major watershed basin of Armenia.
Results and Discussion
The scenario analysis revealed significant variations in ecosystem service delivery under different hypothetical scenarios. Replacing natural vegetation with bare land, for example, led to increased sediment delivery and reduced baseflow, highlighting the protective role of forests and grasslands in maintaining soil stability and water supply.
The Urban Flood Risk Mitigation model indicated that natural vegetation could lower flood risk, protecting densely populated areas from extreme rain events.
Presented in the form of maps and connected statistical reports, these scenarios are very illustrative of the role of ecosystems and how dramatic the effects of forest destruction or large-scale agricultural expansion can be. So, to maximize the impact of our findings, we utilized the open-source Web GIS framework NextGIS Web to publish the results as interactive web maps. Its advanced integration with desktop tool QGIS, which we used as a primary tool of mapping, data preparation and post-processing, saved a lot of time — once being prepared in QGIS, maps are ready to be published on the web. From the point of view of end-users, this platform allows them to explore the data visually on the web-maps, fostering greater understanding and engagement. By making the results publicly accessible, we aim to raise awareness about the importance of ecosystem services and encourage informed decision-making among policymakers and the general public.
Our study demonstrates the utility of scenario-based modeling and Web GIS in supporting sustainable resource management. The insights gained from the InVEST models can inform policy decisions in several key areas such as forest management, water resource management, agricultural practices and urban planning. Open-source technologies and public domain data as a core of the study open wide prospects for reproducing similar research for any other country.
Source data and modeling outcomes are available for public access here: https://bccarmenia.nextgis.com/resource/113/display?panel=layers</abstract>
<slug>foss4g-europe-2025-3966-modeling-ecosystem-services-in-armenia-using-invest-a-scenario-based-approach-with-nextgis-web-integration-for-public-awareness-and-engagement</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/CJGTAD/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/CJGTAD/feedback/</feedback_url>
</event>
<event guid="31f77950-8a77-5214-93dc-36783f38f49f" id="3998">
<room>PA01</room>
<title>Implementation of a 2D/3D WebGIS for Electricity Network Management System</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>Geospatial data represents a critical tool for decision-making processes. The United Nations has recognized the importance of geospatial data through its global goals. The issue of energy is of particular significance for the development of the global community and the establishment of the smart city concept. Therefore, special attention should be given to managing the processes of electricity production and distribution, with a particular focus on designing, simulating the power distribution network and managing electricity losses during transmission.
The planning of the distribution network is an essential element of urban planning and must be comprehensively assessed to improve decision-making procedures as demonstrated in the study by Zheng et al. in China in 2012. Villacres et al. stated that in electrical distribution system planning, wire length is a key parameter for calculating voltage loss and related power losses. Thus, an adequate network topology structure is necessary to execute algorithms and obtain data on losses. For visualization and proper analysis, appropriate open-source technologies can be used. La Guardia et al. provide an example of real-time data integration into a 3D geospatial web-based visualization platform developed with open-source technology. Amović et al. propose executing process parallelization algorithms to speed up system performance.
In 2019, the Republic of Srpska implemented the project "Development of a Utility Cadastre Model," establishing an appropriate framework based on the Utility Network Inspire Directive model. "Elektrokrajina a.d." is a power distribution company supplying half of the consumers in the Republic of Srpska. The aim of this research is to present a study of the system for planning, managing and evaluating the LV/MV power network based on the 2D/3D WebGIS ELMAP system, which acts as a decision-making tool.
The goal of using these tools is to optimize electricity consumption, identify zones and time periods indicating electricity usage patterns leading to the optimization of scheduling and renewal of certain infrastructure elements, redefining the topology of the power network, identifying zones for new transformer station construction and defining new transformer service areas. To efficiently establish such a system it is necessary to address the integration problem of large amounts of 2D/3D data on one side and semi-structured real-time data on the other. These data for power consuption and losses are obtained through other system components via sensor readings.
ELMAP is the central unit of the power distribution information system. It acquires and structure structured, semi-structured, and unstructured data from other components of this information system into the ELMAP model. For these purposes, specific procedures have been developed to structure data extracted from SAP, Stone, Asset Management, and MdM systems, as well as integrate data obtained through geodetic and LiDAR surveying of the power infrastructure. Given the vast amounts of data involved, process parallelization algorithms have been developed. At the PostgreSQL level, serial queries have been implemented, significantly increasing transaction execution speed in the system compared to traditional methods.
Existing geospatial data has been generated through digitization from existing plans or orthophotos. The primary issue with these data is their topological inconsistency. There is no clearly defined geospatial hierarchy of the network in terms of the topological definition of the medium-voltage and low-voltage networks, nor the structuring of power lines, branches, and segments, as well as their connections to transformer stations, poles, and metering points. Since the medium-voltage and low-voltage networks operate at different voltage levels, a dedicated algorithm has been developed to track network topology and voltage changes at transformer stations.
The position of electricity meters in the low-voltage network is determined based on GPS data from a mobile application. The meter is positioned within or touching a building (OSM Buildings are used). These meter positions have subsequently been used as a control mechanism for future meter readings. For system management, an algorithm for determining meter reading routes was developed. The spatial distribution of meters and the road network topology extracted from OSM served as the basis for developing an optimization algorithm for field meter reading routes. For this purpose, the pgRouting environment was implemented to determine the shortest distance, utilizing the All Pairs Shortest Path and Johnson’s Algorithm for finding the shortest path for reading a group of meters.
The ELMAP system model was established as an extended package of the Inspire Directive model (Utility Network – Electricity) to provide an appropriate platform capable of communicating with other system components. The system is designed as a service-oriented three-layer architecture using PostgreSQL with PostGIS extensions as the DBMS, while communication occurs via WFS services and API modules with other system units. The system provides an appropriate administrative and user platform for data management.
To adequately analyze losses in the existing infrastructure network, data on voltage changes at the transformer service area and power line levels are used, expressed through algorithms that provide parameters such as SAIFI, SAIDI, and peak power, indicating network losses. To spatially identify critical points in the infrastructure regarding network overloads and peak consumption periods system can suggest optimization measures and potential changes to transformer service zones.
For 3D structuring and analysis, geospatial data sources include DEM data for terrain topography representation, OSM buildings and roads for object and road representation, integrated through the MAPBOX environment via appropriate API services. Additionally, data on poles, transformer stations, transformer positions, and power lines for the medium-voltage network, as well as poles, power lines, and meters for the low-voltage network, are utilized.
For 3D visualization, the MAPBOX environment was used, where all infrastructure elements were created as assets in GLB format. Given the need for visualizing large amounts of 3D data and models, all elements were structured as 3D Tiles, enabling fast and efficient 3D visualization and analysis of these data.
The accuracy assessment of the structured network topology was conducted by controlling digitized system elements by comparing to data gained terrestrial measurement and calculating RMSE parameters.</abstract>
<slug>foss4g-europe-2025-3998-implementation-of-a-2d-3d-webgis-for-electricity-network-management-system</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/EDTY9C/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/EDTY9C/feedback/</feedback_url>
</event>
<event guid="a1494f37-cc6a-5a7f-84a0-01e6a6a0a5c5" id="3979">
<room>PA01</room>
<title>The challenges of reproducibility for research based on geodata web services</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Digitalization and collaborative approach drives Open Science, the modern way of conducting research. In fact, Open Science can be defined as “a collaborative culture enabled by technology that empowers the open sharing of data, information, and knowledge within the scientific community and the wider public to accelerate scientific research and understanding”. Its three major objectives are: (a) increase the accessibility to the scientific body of knowledge, (b) increase the efficiency of the processes to share research outputs and findings, and (c) improve the evaluation of the science impact considering new metrics. Due to technological advances of the last decades, modern research is, today, mainly data-driven therefore Open Research Data (ORD), which refers to "the data underpinning scientific research results that has no restrictions on its access, enabling anyone to access it." [1], is extremely important.
With means to openly share data, the intent is to accelerate and boost new findings and innovations, minimizing data duplications and enabling interdisciplinary and wider collaborative research. To be effectively used by other researchers, ORD need to follow the specific principles of Findability, Accessibility, Interoperability, and Reuse (FAIR) [2] which led to the creation of data repositories that permits to register, store, find and access data following interoperable metadata standards. Available repositories offer services which generally adhere to ORD best practices by offering open data access, associating a license to data, making them persistent, providing unique citable identifiers (DOI), adopting repository standards, and providing a defined data policy. Nevertheless, in most of those repositories it is only possible to deposit static files, preferably archived using standard open formats and metadata.
However, to fully exploit ORD with modern applications, using for example AI techniques, big data requires specialized services that offer a systematic and regular delivery of Analysis Ready Data (ARD) and filtering capabilities [3]. Sharing ARD perfectly fit with the European vision of establishing Data Spaces as an interoperable digital place to facilitate data exchange and usage in a secured and controlled environment among different disciplines with the goal of boosting innovation, economic growth and digital transformation [4]. This concept goes beyond the simple technical data sharing issues and encompasses the need of offering a space to share data that is compliant with privacy and security regulation.
In the geospatial context, operational data sharing has been implemented by means of Spatial Data Infrastructures (SDIs). They have been implemented based on sharing principles which led to the adoption of interoperable geoservices by which today thousands of geospatial layers are offered to millions of applications worldwide adopting interoperable geostandards that are mainly from the Open Geospatial Consortium (OGC). The technological growth in the last decades led to the explosive increment of time-varying data which dynamically change to represent phenomena that grows, persists and decline, or that constantly vary due to data curation processes that periodically insert, update, or delete information related to data and metadata.
Therefore, based on the current trends, the ability to link Open Science concepts with interoperability and time-varying data management is paramount. In particular, the capability of obtaining results consistent with a prior study using the same materials, procedures, and conditions of analyses is very important since it increases scientific transparency, fosters a better understanding of the study, produces an increased impact of the research and ultimately reinforces the credibility of science. In the Open Science paradigm this is indicated as Reproducible Research, and it can be guaranteed only if the same source code, dataset, and configuration used in the study is persistently available. For geospatial data, while the presented OGC standards enable an almost FAIR [5] and modern data sharing, they do not adequately support the reproducibility concept as pursued in Open Science. In other words, they do not offer any guarantee that the geodata accessed in a given instant in a geoservice can be persistently accessed, immutably, in the future.
The needs and practices of time-varying data updates is supported by real case examples related to common operations that update data or metadata of the different geospatial data types, for example, specifically: environmental and climate data for sensor observations, cadastral and OSM data for vector datasets and satellite derived land cover, crop maps and observations of water for raster series. From a technical perspective, the capacity of accessing data as they were in a specific previous state is strictly linked to the capacity of supporting data versioning. Feature found in specific tools and approaches that largely differs from data formats and storage (databases, files and Log-Structured Tables) bur rarely support geospatial data.
While a defined approach to support system-time exists in SQL and LSTs [7] it is not yet currently adopted on commonly used storage solutions like those offered by the OSGeo’s projects [8] and/or are easy to integrate in them without new software development.
We conclude that OGC Geospatial web services that are currently used in Spatial Data Infrastructures do not meet the reproducibility requirement set by Open Science since they do not guarantee the immutable access to a dataset in its status at a specific time of consumption.
To support this capability, we propose that geospatial data management infrastructures manage datasets versioning and expose these features to users trough standard Web services. Since versions number may evolve extremely fast and are not meaningful to the user, the system time, which identifies the instant for which archived information had specific values, should be used, in conjunction with web service URL, as a unique identifier of the dataset. Finally, together with the support of versioning we propose to support the git-like metadata on user and motivation on data transactions: this would greatly support reproducibility and Open Science. In fact, it would not only allow us to retrieve temporal versions of the dataset, but it would also permit us to perform data lineage analysis to fully understand the historical changes and better comprehend the dataset (including data provenance and ownership) with the effect of fostering the transparency and, ultimately, the credibility of science.</abstract>
<slug>foss4g-europe-2025-3979-the-challenges-of-reproducibility-for-research-based-on-geodata-web-services</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/9AMAMN/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/9AMAMN/feedback/</feedback_url>
</event>
<event guid="26ff310c-1f65-56be-a435-d413789487a8" id="3988">
<room>PA01</room>
<title>Monitoring the FAIRness of geospatial data: Lessons learnt from the European Union</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-16T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Over the last decade, the global landscape of data sharing has gone through profound changes that also applied to the geospatial domain, where traditional Spatial Data Infrastructures (SDIs) have progressively evolved into multifaceted data sharing ecosystems [1]. These ecosystems embraced fundamentally new elements in terms of: (big) data sources (e.g. from research, Earth Observation, Internet of Things devices, crowdsourcing initiatives, synthetic data from Artificial Intelligence/Machine Learning algorithms or Digital Twins); technology and infrastructures (e.g. cloud/edge/fog architectures, standards to encode and share data, AI/ML models); actors such as private companies and citizens becoming valuable providers of data and services; legislation (e.g. to open up data, foster data sharing and protect privacy); and business models and governance mechanisms [2,3].
Within this modern and dynamic context, it becomes increasingly important to setup targeted, fit-for-purpose, and efficient mechanisms to monitor the status and evolution of SDIs to extract the insights needed by policy and decision makers. In this work, we present the experience of monitoring the implementation of the European SDI established after the INSPIRE Directive, reflect on the lessons learnt from the process, and distill some recommendations for future policy-relevant scientific work.
In force since 2007, the INSPIRE Directive [4] set the legal basis to create an interoperable pan-European SDI based on the SDIs of the European Union (EU) Member States, with legal and technical requirements on the FAIRness (Findability, Interoperability, Interoperability, Reusability) of data: discoverability through metadata, accessibility through network services, and interoperability through common data models. The status of implementation for each Member State is assessed through an annual monitoring exercise, in which 19 indicators defined in a legal act [5] are calculated based on the metadata harvested each year from the Member States catalogues. These are grouped in 5 categories focused on: i) availability of datasets, ii) conformity of metadata, iii) conformity of datasets, iv) accessibility of datasets, and v) conformity of network services. Since the entry into force of the legal act on INSPIRE monitoring [5] in 2019, the Joint Research Centre (JRC) of the Commission has calculated those indicators for 6 consecutive years, thus producing a valuable time series from which to derive insights and lessons learned, which in the following are grouped in three categories: i) geospatial resources (data, metadata and services); ii) tools/technology and iii) community/governance.
Regarding the resources shared by the Member States, over the last 6 years INSPIRE implementation has overall advanced. Although it remains heterogeneous across countries, aggregated statistics on the indicators show that more datasets have been made available and these are increasingly more interoperable and accessible. Nevertheless, challenges remain as e.g. i) some indicators rely on self-declarations of conformity made by data providers, which were proven to be unreliable; ii) indicators are provider-centric, i.e. they describe the offer from data providers but not their actual adoption and reuse by the public; iii) they do not analyse the quality of the datasets; and iv) while capturing the amount of available datasets, they do not capture the presence or lack of specific datasets. Research should investigate ways to address these challenges without increasing the required effort, e.g. by leveraging new, AI-based solutions.
Tools and technology have played a crucial role in the INSPIRE monitoring process. The calculation of indicators has been fully automated and the software stack has evolved over the years and currently includes all open source applications: the [INSPIRE Geoportal](https://inspire-geoportal.ec.europa.eu/srv/eng/catalog.search#/home) based on [GeoNetwork](https://geonetwork-opensource.org/), the [INSPIRE Reference Validator](https://inspire.ec.europa.eu/validator/home/index.html) based on the [ETF](https://github.com/etf-validator), and a set of custom-made open source Python and SQL [scripts](https://github.com/INSPIRE-MIF/mr-tools). The open source nature of the components, with clear release processes allowing data providers to test their implementations in advance, ensures objectivity, transparency and reproducibility of results. The use of a reference validation tool also brings legal certainty to the process. Additionally, Large Language Models (in particular, the open-source Mixtral) have proven extremely useful in refining, testing, and validating results. Finally, the monitoring process has benefited from integrating newly developed standards such as OGC API - Features for data sharing and GeoPackage for data encoding, enabling Member States to streamline and modernise their infrastructures in a legally viable way.
Finally, the success of the INSPIRE monitoring exercise, an iterative process undergoing incremental changes over the years, relies on establishing a continuous dialogue and building trust with the relevant community. This is achieved through a clear governance structure, provision of open and scientifically sound guidance on indicator calculation, clear explanation of results, and targeted, country-specific feedback on potential improvement areas. The process has also highly contributed to the evolution of the open source tools mentioned above.
The lessons learned from this unique SDI initiative, with no equivalent in temporal and spatial extension, can inform and benefit similar initiatives, while also highlighting the need for specific scientific and technological advancements, including in the field of open source, to further reduce the distance between data and (data-driven) decision-making.</abstract>
<slug>foss4g-europe-2025-3988-monitoring-the-fairness-of-geospatial-data-lessons-learnt-from-the-european-union</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8GJRDX/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8GJRDX/feedback/</feedback_url>
</event>
<event guid="29762043-fe9b-5505-98f3-96a74a384ca9" id="3996">
<room>PA01</room>
<title>Honey, I shrunk the GIS – Developing scalable and lightweight geospatial software applications with microservices and containerization</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-16T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:05</duration>
<abstract>Traditional Geographic Information Systems (GIS) are often developed as monolithic software. This project presents a case study that explores the development of geospatial software using microservices and containerization, focusing on core modular elements such as images, containers, and service orchestration. The use case involves serving geospatial data related to radiological measurement data for routine monitoring and emergency response scenarios. The implementation is fully open-source and includes the following technologies: Docker, PostgreSQL/PostGIS, GeoServer, Node.js, and OpenLayers. The primary aim is to share practical insights into modular software development and provide a streamlined guide for rapidly building lean, portable and maintainable geospatial applications.
Background and Motivation
Monolithic software development poses numerous limitations compared to component-based approaches. In the proposed architecture, all key GIS components - data storage, management, and visualization - are modularized and isolated into independent services. Each service is encapsulated within its own Docker container, simplifying deployment and ongoing maintenance. This isolation enables independent development of each component, provided that the interfaces (e.g., APIs) remain consistent.
For example, by fully integrating a relational database management system (RDBMS) such as PostgreSQL/PostGIS, GIS applications can delegate core functions—including user and role management, query optimization, data storage and retrieval, and backup and recovery—to the database layer. This eliminates the need to build such functionality from scratch, promoting a concept known as decoupling. Decoupling is central to tiered software architectures as it minimizes interdependencies, improves flexibility and enables autonomous development across components.
Microservices and Containerization in GIS
The architecture presented in this study organizes components like data storage and visualization into a multi-service environment using Docker. Docker provides an ecosystem that encapsulates the operating system and all required dependencies, enabling OS-level and software-level virtualization (Acharya et al., 2021). Key benefits of this approach include:
• Portability - containers encapsulate all required libraries, dependencies, software and configurations.
• Efficiency - containers use fewer resources than VMs or server infrastructures. They also have a positive effect on human resources because they lessen dependencies between developers.
• Scalability - supports both horizontal and vertical scaling.
• Isolation - containers are self-contained and reduce the risk of conflicts.
• Continuous integration - testing and production environments can be identical, accelerating development cycles.
• Clear ownership - containers have well-defined boundaries, which prevents individuals from interfering with each other’s work and thereby improving efficiency.
• Interoperability - containers and services can easily be consumed with no special needs in terms of operating system or software.
The project implements a 4-tier container orchestration to visualize the geodata, PostgreSQL/Postgis for data storage, pgAdmin as Postgres’ graphical user interface (GUI), Geoserver as a web map server and Node.js for client dependencies such as OpenLayers.
Components can be individually configured and tailored. In a containerized environment, services are defined as images and deployed as containers. These can be distributed and executed independently of the host system's configuration. For example, deploying a traditional web-based GIS requires manually installing and configuring a database (e.g., PostgreSQL), a web map server (e.g., GeoServer), and a frontend visualization library (e.g., OpenLayers or Leaflet). This process must be repeated for each redundant server to achieve fault tolerance or switchover capabilities - an inefficient and error-prone approach.
By contrast, containerization allows each component to be defined in configuration files (e.g., docker-compose.yml) alongside accompanying text files for credentials and settings. The result is a streamlined and reproducible environment where services interact through well-defined interfaces. Containerization enables full-stack development of service-oriented architectures (SOA), simplifying workflows needed for redundancy, fault tolerance and system maintenance. Modularization also facilitates all key tasks across the GIS stack - from data ingestion and analysis to visualization.
By isolating each functional component - data ingestion, storage, service and visualization into independent Docker containers, the system achieves modularity, scalability, and ease of maintenance. The use of open-source tools like PostgreSQL/ PostGIS, GeoServer, and OpenLayers ensures adaptability to a wide range of GIS use cases. This architecture not only simplifies development and deployment but also provides a robust foundation for building more complex geospatial systems that support advanced spatial analytics and statistics or elaborate data pipelines.
Real-World Application: Radiological Emergency Preparedness
The application showcases how radiological measurement data can be deployed for various tasks from routine monitoring to emergency response. The Federal German Office for Radiation Protection (Bundesamt für Strahlenschutz) is responsible for detecting, assessing, and reacting to nuclear and radiological events. It collaborates with European member states and international agencies (e.g., Euratom, IAEA). In an emergency, it must rapidly collect, analyze, and distribute information while proposing protective measures to reduce the impact of nuclear fallout.
Scalability is critical, as emergency situations often trigger surges in web traffic. The agency has prioritized container-based, component-driven software architectures for nearly a decade, yielding significant improvements in continuous integration and deployment (CI/CD), scalability, maintainability, portability, resilience (e.g., service recovery) and overall performance.</abstract>
<slug>foss4g-europe-2025-3996-honey-i-shrunk-the-gis-developing-scalable-and-lightweight-geospatial-software-applications-with-microservices-and-containerization</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/7H7P38/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/7H7P38/feedback/</feedback_url>
</event>
<event guid="28c1c361-cb90-5521-865f-9eef341b0a62" id="3987">
<room>PA01</room>
<title>An approach that utilizes blockchain to effectively and securely preserve data privacy for location data from IoT in smart cities.</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-16T16:35:00+02:00</date>
<start>16:35</start>
<duration>00:05</duration>
<abstract>1. Introduction
Geospatial data has become an essential part of modern technological ecosystems, fuelling advancements in areas such as navigation, logistics, disaster management, and smart city planning. The capacity to capture and utilize accurate location information has significantly improved decision-making processes, resource allocation, and real-time situational awareness. However, as the use of geospatial data expands, so do the associated risks of storing, transmitting, and processing this information.
Blockchain technology offers enhanced security, transparency, and decentralization, which holds transformative potential for geospatial data sharing. The quality and trustworthiness of shared data have been damaged due to the lack of transparency and credibility of intelligent sources. The objective is to Geospatial data management is being revolutionized by blockchain technology, which provides a decentralized and tamper-proof framework for data provenance. Users can trust the data they access by using this immutable record of data lineage, which enhances traceability and establishes clear data ownership.
This paper introduces a combination of Blockchain technology (to ensure transparent data integrity) and cryptography (to provide data confidentiality) as a solution to these issues. We present a prototype implementation of this proposed scheme as well as corresponding experimental results obtained with an actual smart city data set.
2. Technology Used
2.1 Blockchain
Blockchain technology has emerged as a promising avenue for addressing the security and integrity demands of sensitive data. By distributing information across a network of nodes, blockchains offer immutability, transparency, and decentralized governance. Yet, implementing a blockchain-based platform for geospatial data introduces several key challenges. Large datasets, complex retrieval requirements, and the computational overhead of blockchain transactions can complicate system design.
2.2 Cryptographic
Cryptographic methods, specifically AES (Advanced Encryption Standard) for symmetric encryption and RSA (Rivest-Shamir-Adleman) for asymmetric encryption, have long been used by industries to protect digital information. Efficient end-to-end encryption and secure key management are crucial for confidentiality and integrity in a blockchain context when combined with AES and RSA in a hybrid cryptosystem. Moreover, Proof-of-Location (PoL) protocols such as FOAM add a novel layer of authenticity by verifying the real-world coordinates that underlie each data entry, making it substantially more difficult for attackers to spoof or tamper with location records.
2.3 Web3 and Open Standard Protocols
The development of innovative geospatial applications and markets can also be facilitated by Web3. Open standards for consensus-driven mapping and proof of location are provided by decentralized platforms like the FOAM protocol, which enable the creation of thrustless geospatial data ecosystems. The geospatial space has several noteworthy Web3 projects, including Shamba, a decentralized geospatial data oracle, and Geodnet, a decentralized network for sharing and monetizing geospatial data.
2.4 Smart Contract
A smart contract is a contract that can be executed by itself, with code embedded with rules and agreements, and deployed on a blockchain. Transacting without intermediaries is assured through thrustless, automated, and tamper-proof transactions. The automation of data access control and the enforcement of predefined rules can be done by smart contracts, ensuring that data is shared only with authorized participants.
2.5 Used Technology in paper
In this paper, a comparative analysis compares the performance, computational overhead, and scalability of AES and RSA. Factors such as encryption/decryption speed, memory footprint, and transaction costs determine blockchain's suitability for large-scale geospatial datasets. A prototype system has been created to show the complete process, from encryption and blockchain-based storage to on-demand retrieval and secure decryption, while keeping sensitive location information confidential and intact.
3. Data Integrity and Access Control
Web3 technologies, based on blockchain principles, are advancing the decentralized storage and processing of spatial data. Decentralized file systems like IPFS and Filecoin enable the distributed storage of large geospatial datasets, ensuring data availability and resilience. By removing single points of failure, these systems allow for efficient data retrieval and sharing.
In addition, blockchain enables the secure sharing of data between peers without relying on centralized authorities. By directly exchanging geospatial data between parties, there is no need to employ intermediaries, and there is a reduced risk of data tampering.
Blockchain technology, however, does not provide proper access control to data stored within the blockchain. Therefore, data stored within the blockchain needs to be encrypted in order to ensure that data can only be read by proper entities (access control).
4. Experiment
The objective of this paper is to combine blockchain technology and cryptographic techniques to guarantee data integrity as well as access control for geospatial data when storing and transmitting it. The research examines the architecture of how encrypted geospatial coordinates can be committed to a blockchain, ensuring immutability while maintaining controlled access. Smart contracts ensure that data-sharing policies are enforced and user permissions are validated, thereby decreasing the need for centralized intermediaries. The practical viability of this system has been evaluated by a proof-of-concept prototype that includes metrics like encryption/decryption speed, on-chain data overhead, and access latency.
A comparative analysis of centralized vs. is used in the research to evaluate the proposed framework. Security, scalability, and performance are the main concerns of decentralized approaches.
This involves analyzing various encryption techniques and quantifying the difference between on-chain and off-chain. The feasibility of large-scale geospatial datasets is tested within the constraints of blockchain networks through off-chain storage trade-offs. The study investigates the role of Proof-of-Location protocols in enhancing location authenticity and resilience against spoofing.
5. Conclusion
To sum it up, this paper is aimed at offering a complete solution to the long-standing problem of securely managing geospatial data, covering everything from cryptographic confidentiality to blockchain immutability and location verification. The goal of the work is to demonstrate the technical feasibility and broader impact of integrating AES, RSA, and Proof-of-Location into real-world geospatial applications by combining them on a blockchain platform. The paper details the design and implementation of a system, demonstrates empirical results, and discusses future developments for scalability and regulatory compliance.</abstract>
<slug>foss4g-europe-2025-3987-an-approach-that-utilizes-blockchain-to-effectively-and-securely-preserve-data-privacy-for-location-data-from-iot-in-smart-cities-</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/A88X7L/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/A88X7L/feedback/</feedback_url>
</event>
<event guid="7735388c-9a0f-59eb-a3e3-276d4d39ba89" id="3969">
<room>PA01</room>
<title>Challenges and Opportunities in University-Based Humanitarian Mapathons: Enhancing Citizen Science Contributions to Open Spatial Data</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-16T16:40:00+02:00</date>
<start>16:40</start>
<duration>00:05</duration>
<abstract>Citizen science has emerged as a pivotal paradigm for the generation and refinement of digital geographic data, particularly within regions often overlooked by commercial mapping initiatives (Vohland et al., 2021). This is especially critical in areas characterized by rapid urbanization, humanitarian crises, or environmental vulnerability, where up-to-date and accurate spatial information is paramount for effective planning, disaster response, and sustainable development. Within this context, mapathons, defined as organized collaborative mapping events, have gained considerable traction within humanitarian and development communities. These intensive mapping sessions leverage the collective effort of volunteers to contribute to platforms like OpenStreetMap (OSM), a free and open-source geographic database of the world (Mooney &amp; Mingini, 2017). Universities, as hubs of knowledge, innovation, and community engagement, have increasingly become vital centres for the establishment and operation of humanitarian mapping communities, often driven by student interest in applying geospatial skills to address real-world challenges.
This paper delves into the complex landscape of organizing and executing mapathons within university settings, drawing upon empirical observations and practical experiences primarily gathered in Czechia, with a specific focus on Tomas Bata University (TBU) as a representative example of a regional university deeply engaged in community outreach and regional development initiatives. Utilizing the framework of OpenStreetMap and the Humanitarian OpenStreetMap Team (HOT) Tasking Manager – essential tools for humanitarian mapping – this research undertakes a comprehensive analysis of the multifaceted challenges and opportunities inherent in university-based mapathons. The methodology combines quantitative analysis of mapping contributions with qualitative assessment through participant feedback collected during multiple mapathon events at TBU between 2019 and 2024.
The paper is structured around several key research questions designed to illuminate the critical factors influencing the efficacy and impact of these events. Firstly, we explore the intrinsic and extrinsic motivations that drive university students to participate in humanitarian mapping activities. Understanding these motivations is crucial for designing effective recruitment and retention strategies (Štampach et al., 2021). A critical yet understudied challenge in sustaining engagement lies in harnessing narrative methods to amplify intrinsic motivations among participants. In this context, storytelling seems to be a potentially promising method. Participants are more interested in the quality of the task completed, but in addition, they are inspired to perform and engage in other activities with a similar story they can emotionally empathize with. This emotional connection appears to significantly enhance commitment levels and mapping persistence over time.
Secondly, we examine the inherent limitations and learning curves associated with mapping contributions from both novice and experienced mappers within a mapathon setting. This includes an assessment of data quality, mapping accuracy, and the types of errors commonly introduced by mappers with varying levels of expertise. Thirdly, we analyze the factors that enhance or diminish the attractiveness of individual mapping tasks presented within the HOT Tasking Manager. This encompasses the geographical context of tasks, the perceived impact of contributions, and the clarity and scope of mapping instructions.
Beyond these human-centric aspects, the paper also grapples with technical challenges that can impede the smooth operation and effectiveness of university mapathons. One significant constraint explored is the limitations imposed by changeset size restrictions for new OSM contributors, a feature designed to manage data quality but potentially hindering the productivity of large-scale mapathons. Furthermore, we rigorously assess the influence of different software interfaces on mapper performance and data quality, comparing the widely used web-based iD editor with the more feature-rich desktop application JOSM (Java OpenStreetMap Editor).
Finally, the study investigates the impact of the geometry and complexity of vectorized objects on mapping efficiency and accuracy. We specifically consider the relative challenges associated with mapping different feature types such as roads (linear), buildings (polygonal), and land use areas (polygonal), and how these geometric characteristics affect the overall mapathon workflow. The various elements have different requirements for accuracy and mappers' experience, and some objects are easier for beginners and vice versa. Appropriate choice of mapping tasks can contribute to better participation and overall efficiency of geographic data production.
The preliminary results of this research strongly indicate that well-structured mapathon activities, coupled with robust community engagement strategies, can substantially augment both the level of student engagement and the overall scope and quality of volunteered geographic information (VGI). The foundation for these results is also data from the mapathons already held at Tomas Bata University, which point to a significant amount of recorded geospatial data. Through the coordinated involvement of a large number of participants over a period of time, the mapping has a significant impact on certain humanitarian activities in endangered and unheeded places on Earth. Our analysis indicates that structured university mapathons can achieve notably higher feature completion rates and lower error rates compared to ad-hoc mapping efforts, particularly when incorporating targeted training components.
By meticulously addressing the identified challenges and capitalizing on the inherent opportunities within university environments, we can significantly enhance the contribution of citizen science to global geospatial data to the benefit of both the mappers themselves and the users of the collected data. This research contributes to the growing body of knowledge on participatory mapping methodologies while offering practical guidance for university stakeholders seeking to establish or enhance humanitarian mapping initiatives within their institutions.</abstract>
<slug>foss4g-europe-2025-3969-challenges-and-opportunities-in-university-based-humanitarian-mapathons-enhancing-citizen-science-contributions-to-open-spatial-data</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/7SCFJG/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/7SCFJG/feedback/</feedback_url>
</event>
<event guid="6e558a55-2d22-5d3a-9b0e-d7c4eb73102c" id="4002">
<room>PA01</room>
<title>Python plugin for statistical analysis of landslides susceptibility over wide areas</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-16T16:45:00+02:00</date>
<start>16:45</start>
<duration>00:05</duration>
<abstract>Within the Extended Partnership “Multi-Risk sciEnce for resilienT commUnities undeR a changiNg climate” (RETURN), the research group of the Department of Civile, Chemical and Environmental Engineering of the University of Genova is developing a system for processing landslide susceptibility maps in GIS environment.
The expected result is a tool meant to be used by administrations and local authorities for ground instability assessment and management. Therefore, high usability is required, that implies free and easily available base data, easily interpretable results, and clearly explained limitations of use and reliability level.
To accomplish this objective, some stakes have been established:
• Only earth landslide are expected to be considered for the processing of susceptibility maps. They include slow flows, fast flows, slides, areas subject to diffuse shallow landslides and those landslides classified as “undetermined” and “complex” that generally contain at least some earth movement .
• The proposed tool should be scalable and transferable. To such purpose, eight independent predisposing factors were chosen complying with these requirements and described by data open and available for the whole Italy.
• To ensure the quality of input and output data, the procedure is optimized for certain “certified” datasets
• The procedure has to be transparent and traceable . The logistic regression method was chosen, which is widely used and described in the literature. The resulting maps report probability values that are quite difficult to understand, so, to facilitate usability, they are subsequently aggregated into qualitative susceptibility classes.
• Minimum reliability threshold: the procedure has been tuned, and additional tests are presently in course in some study areas, to ensure an overall reliability defined through AUC of at least 75%. In case of lower AUC values, the causes are investigated, to foresee possible corrective interventions.
• In order to allow also people not particularly experienced in GIS to use the model and to ensure the correct implementation of the planned operations, the writing of the entire procedure in Python code is underway, at the moment within GRASS, then, possibly as a QGIS plugin.
The procedure was tested in GRASS over areas of about 1,000 km2, considering the pixel as the minimum spatial unit, with a nominal scale of 1:100,000 and a raster resolution of 20 m. The first phase consisted of the preprocessing and discretization of the basic data, so as to allow a general control of data quality and reduce the possible combinations of factors to a manageable number. The eight considered factors, elevation, slope, aspect, water accumulation, land use/land cover, lithology and rainfall influence, were then brought into raster format at the set resolution and divided into qualitative (e.g., land use type) or ordinal (e.g., Elevation Intervals, from 0 to maximum elevation) classes.
The resulting maps were compared in a bivariate analysis with the Inventory of Landslide Phenomena In Italy (IFFI), and the classes of each factor were reordered on the basis of conditional probability. The factors were then related to each other and to actual landslides in a multivariate analysis by logistic regression, defining for each pixel the probability of landslide occurrence. The obtained values were finally grouped into three qualitative classes to indicate high, medium and low landslide susceptibility.
The procedure described above resulted in an AUC in calibration generally above the preset threshold of 75 percent and was used as a basis for the realization of the actual tool, through a series of refinements currently in progress.
The tests so far have shown that each type of landslide is affected differently by the factors considered, and the model is more reliable if the different kinematisms are treated separately. Therefore, for each study area, the procedure has to be repeated for all landslide types, which is time-consuming and disk room-consuming. In response to this problem, the rewriting of the model as a python script is in course, not only for a more efficient application, but also to define a standardized procedure by which to make it accessible to people outside the research team, and leading to comparable results.
As part of the automation of the procedure, efforts are also being made to define ancillary functions dedicated to solving problems that arose during the experimentation.
With regard to the definition of the statistical sample, i.e., the areas actually in landslide, the need has emerged to distinguish, for the phenomena reported in the IFFI repository, the detachment area, i.e., the area from which the landslide actually developed, and the accumulation area, i.e., the part of the territory affected by the effects of the landslide. Therefore, to avoid introducing noise into the model due to incorrect perimetry, a part of the code is being prepared to separate the two parts on a statistical basis.
Another issue that is being addressed is the identification, for each kinematism and for each territory considered, of the factors actually determining the development of the landslides, in order to reduce noise and lighten the computational processes. Several methods are being tested, including “Frequency Ratio,” “Leave One Out,” and “Stepwise”. Once the most effective method is determined, this part will also be introduced as a script into the model.</abstract>
<slug>foss4g-europe-2025-4002-python-plugin-for-statistical-analysis-of-landslides-susceptibility-over-wide-areas</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FRJ3M3/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FRJ3M3/feedback/</feedback_url>
</event>
</room>
</day>
<day index="2" date="2025-07-17" start="2025-07-17T04:00:00+02:00" end="2025-07-18T03:59:00+02:00">
<room name="EL11" guid="e2a70c93-299e-589b-b24b-95b372b81974">
<event guid="f90ba73b-a375-5d28-90ad-92123b8f8fb8" id="3176">
<room>EL11</room>
<title>EarthCODE - a FAIR and Open Environment for collaborative research in Earth System Science</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>The Open Science and Innovation Vision included in ESA’s EO Science Strategy (2024) addresses 8 key elements: 1) openness of research data, 2) open-source scientific code, 3) open access papers with data and code; 4) standards-based publication and discovery of scientific experiments, 5) scientific workflows reproducible on various infrastructures, 6) access to education on open science, 7) community practice of open science; and 8) EO business models built on open-source. EarthCODE (https://earthcode.esa.int) is a strategic ESA EO initiative to support the implementation of this vision.
EarthCODE (Earth Science Collaborative Open Development Environment) aims towards an integrated, cloud-based, user-centric development environment for European Space Agency’s (ESA) Earth science activities and projects. EarthCODE looks to maximise long-term visibility, reuse and reproducibility of the research outputs of such projects, by leveraging FAIR and open science principles and enabling, thus fostering a sustainable scientific process. EarthCODE proposes a fully open-source flexible and scalable architecture developed with interoperable open-source blocks, with a long-term vision evolving by incrementally integrating industrially provided services from a portfolio of the Network of Resources. EarthCODE platform collaborators participate in creating integrated architecture, with interoperable solutions and federated capabilities. Additionally, EarthCODE is a utilisation domain of EOEPCA+ (https://eoepca.org/), thus contributing to the development and evolution of Open Standards and protocols to enable internationally interoperable solutions.
EarthCODE will provide an Integrated Development Platform, giving developers the tools needed to develop high quality workflows that allow experiments to be executed in the cloud and be end-to-end reproduced by other scientists. EarthCODE is built around existing open-source solutions, building blocks and platforms, such as the Open Science Catalogue, EOxHub and EOEPCA. It has additionally begun to integrate platform services from DeepESDL, Euro Data Cube, Polar TEP and the openEO federation on CDSE platforms, with more being added annually through ESA best practices. With it’s adopted federated approach, EarthCODE will have the capability to facilitate processing on other platforms, i.e. DeepESDL, ESA EURO Data Cube, Open EO Cloud/Open EO Platform and AIOPEN/AI4DTE.
The roadmap for the portal includes the initial portal release by end of 2024, followed by the capability to publish experiments in Q1 2025 (including development, publishing, finding and related community engagement), and by mid-2025 to have a further release with reproducibility capabilities around accessibility and execute functionalities.
Collaboration and Federation are at the heart of EarthCODE. As EarthCODE evolves we expect providing solutions allowing federation of data and processing. EarthCODE has ambition to deliver a model for a Collaborative Open Development Environment for Earth system science, where researchers can leverage the power of the wide range of EO platform services available to conduct their science, while also making use of FAIR Open Science tools to manage data, code and documentation, create end-to-end reproducible workflows on platforms, and have the opportunity to discover, use, reuse, modify and build upon the research of others in a fair and safe way. Overall, EarthCODE aims to enable elements for EO Open Science and Innovation vision, including open data, open-source code, linked data/code, open-access documentation, end-to-end reproducible workflows, open-science resources, open-science tools, and a healthy community applying all the elements in their practice.</abstract>
<slug>foss4g-europe-2025-3176-earthcode-a-fair-and-open-environment-for-collaborative-research-in-earth-system-science</slug>
<track>FOSS4G ‘Made in Europe’</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/GEKTLL/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/GEKTLL/feedback/</feedback_url>
</event>
<event guid="f6628046-5693-5dfd-a4c9-95c0aa6f7bff" id="3511">
<room>EL11</room>
<title>From Code to Climate Solutions - hear more about the 2025 Code for Earth projects</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>Code for Earth, an ECMWF-led partnership programme, drives innovation and collaboration in in Earth sciences, weather, climate, and atmospheric research. By connecting talented developers with Code for Earth mentors, the programme fosters the creation of open-source solutions that support ECMWF activitiesand EU-funded initiatives like Copernicus and Destination Earth (DestinE).
Since its launch in 2018, Code for Earth has facilitated 50 open-source developments, addressing real-world challenges through data science, visualization, analytics, AI/ML, and user support tools. Each summer, a new group of selected individuals and teams contributes fresh ideas and solutions to pre-set challenges to solve. This presentation will present the work of the 2025 participants, showcasing the newly selected teams and their cutting-edge projects. Attendees will also learn how to get involved in future editions and make a meaningful impact on Earth system sciences.</abstract>
<slug>foss4g-europe-2025-3511-from-code-to-climate-solutions-hear-more-about-the-2025-code-for-earth-projects</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/RC8RFD/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/RC8RFD/feedback/</feedback_url>
</event>
<event guid="0e639a52-688f-5010-baeb-06e8ad6c45c3" id="3458">
<room>EL11</room>
<title>Unlocking the value of geospatial data: early insights from the EU Open Data Directive</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Published in 2019, the Open Data Directive (Directive 2019/1024) introduced the notion of high-value datasets. These are datasets from EU public sector organisations that – thanks to their reuse, especially from small and medium-sized enterprises (SMEs) – hold the potential to generate significant socioeconomic or environmental benefits as well as innovative services. As such, the Directive required that high-value datasets are made available free of charge, under open licenses (CC BY 4.0 or any equivalent or less restrictive license), via Application Programming Interfaces (APIs) and, where relevant, as a bulk download.
While the Directive only listed the six categories of high-value datasets (Geospatial, Earth observation and environment, Meteorological, Statistics, Companies and company ownership, Mobility), the subsequent Implementing Regulation – in force since February 2023 and applicable from June 2024 – provided the actual list of datasets to be made available by EU Member States, together with the requirements for their publication, e.g. in terms of granularity, key attributes and metadata. Three out of the six categories of high-value datasets (Geospatial, Earth observation and environment, and Mobility) include datasets with a geospatial nature, which were on purpose defined to match the datasets already in scope of the INSPIRE Directive (Directive 2007/2/EC). This is the Directive, in force since 2007 and currently under revision within the GreenData4All initiative, which established a pan-European spatial data infrastructure. INSPIRE was mostly focused on achieving data discoverability, accessibility and interoperability, but it did not provide requirements on data licensing. The result is that datasets are made available under several different reuse conditions, including only a portion of open data. Therefore, the high-value datasets Implementing Regulation is expected to add an open license requirement to INSPIRE data, thus opening up new opportunities for all stakeholders interested in reusing EU public sector data.
This talk will first describe the high-value datasets Regulation and provide an overview of the geospatial datasets in scope and the requirements on their provision. Afterwards, it will present the results of the first reporting exercise from EU Member States due in February 2025, compare the results with the indicators measuring the implementation of the INSPIRE Directive, and reflecting on lessons learnt and emerging good practices. Finally, the talk will zoom on specific and significant examples of EU public sector data that have been unlocked thanks to the Regulation and hold the potential to drive meaningful impact in various fields.</abstract>
<slug>foss4g-europe-2025-3458-unlocking-the-value-of-geospatial-data-early-insights-from-the-eu-open-data-directive</slug>
<track>FOSS4G ‘Made in Europe’</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/Q8FVUH/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/Q8FVUH/feedback/</feedback_url>
</event>
<event guid="2da6759c-6681-5636-a261-c91ee1fc1d3d" id="3177">
<room>EL11</room>
<title>EOEPCA+: a method for an open-sourced EO Exploitation Platform Common Architecture</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>The ‘Exploitation Platform’ concept derives from the need to access and process an ever-growing volume of data. Many web-based platforms have emerged - offering access to a wealth of satellite Earth Observation (EO) data. Increasingly, these are collocated with cloud computing resources and applications for exploiting the data. Rather than downloading the data, the exploitation platform offers a cloud environment with access to EO data and associated compute and tools that facilitate the analysis and processing of large data volumes. The Exploitation Platform benefits users, data providers and infrastructure providers. Users benefit from the scalability &amp; performance of the cloud infrastructure, the added-value services offered by the platform – and avoid the need to maintain their own hardware. Data hosted in the cloud infrastructure reaches a wider audience and Infrastructure Providers gain an increased cloud user base.
Users are beginning to appreciate the advantages of exploitation platforms. However, the market now offers a plethora of platforms with various added value services and data access capabilities. This ever-increasing offer is rather intimidating and confusing for most users. Users often face challenges such as inconsistent interfaces, proprietary software and limited interoperability. To fully exploit the potential of these complementary platform resources we anticipate the need to encourage interoperation amongst the platforms, such that users of one platform may consume the services of another directly platform-to-platform.
EOEPCA (EO Exploitation Platform Common Architecture - https://eoepca.org/) is a European Space Agency (ESA) funded project with the goal to define and agree a re-usable exploitation platform architecture using standard interfaces to encourage interoperation and federation between operational exploitation platforms - facilitating easier access and more efficient exploitation of the rapidly growing body of EO and other data. Interoperability through open standards is a key guiding force for the Common Architecture. EOEPCA adheres to standards from organisations such as Open Geospatial Consortium (OGC) and follows best practices in data management, including implementation of OGC Web Services and emerging OGC API specifications for features, coverages and processes. Platform developers are more likely to invest their efforts in standard implementations that have wide usage; off-the-shelf clients and software are more likely to be found for standards-based solutions.
The EOEPCA system architecture is designed to meet a set of defined use cases for various levels of user, from expert application developers to data analysts and end users. The architecture is defined as a set of Building Blocks (BBs), exposing well-defined open-standard interfaces. These include Identity and Access Management, Resource Discovery, Data Access, Processing Workflows, Data Cube Access, Machine Learning Operations, and more. Each of these BBs are containerized for Kubernetes deployment, which provides an infrastructure-agnostic deployment target.
The exploitation platform is conceived as a ‘virtual work environment’ where users can access data, develop algorithms, conduct analysis and share their value-adding outcomes. The EOEPCA architecture facilitates this through a Workspace BB that provides collaboration environments for projects (groups of users) including dedicated storage and services for analysis, processing and publishing of added-value data and applications. This is supported by an Application Hub building-block that provides interactive web-tooling for analysis, algorithm development, data exploitation and provides a web dashboard capability through which added-value outcomes can be showcased.
Our presentation will highlight the generalised architecture, standards, best practice and open source software components available.</abstract>
<slug>foss4g-europe-2025-3177-eoepca-a-method-for-an-open-sourced-eo-exploitation-platform-common-architecture</slug>
<track>FOSS4G ‘Made in Europe’</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/AJBHPV/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/AJBHPV/feedback/</feedback_url>
</event>
<event guid="8f20d1b1-470c-5135-b70f-ed748652f480" id="3279">
<room>EL11</room>
<title>How Data Lake services support Destination Earth users - A Year of Insights and Experiences</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>Destination Earth (DestinE) is a flagship initiative led by the European Commission, implemented by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the European Space Agency (ESA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). It aims to create highly detailed Digital Twins (DTs) of the Earth, enabling precise simulations for a variety of uses. Currently, the initiative focuses on two primary Digital Twins: the Weather Extremes Digital Twin (ExtremeDT) and the Climate Change Adaptation Digital Twin (ClimateDT). Over the coming years, the scope of Digital Twins is set to expand, necessitating improved access to data and streamlined methods for working with it. This is where the Destination Earth Data Lake (DEDL) plays a pivotal role, offering comprehensive data discovery, access, and processing services tailored to the needs of DestinE users.
The DEDL operates on two key levels: ‘Data Discovery and Access’ and ‘Edge Services’. DEDL Discovery and Data Access services is provided by Harmonized Data Access (HDA) tool which provides a single, federated entry point to the services and data, including resources from existing datasets and complementary sources such as in-situ and socio-economic data. Notably, it also provides access to the unique datasets generated by DestinE’s Digital Twins. By combining these sources, users can seamlessly explore, integrate, and analyze both existing services and the innovative data produced by the Digital Twins. What is more, all this data is provided as a full archive immediately available to the user. The services rely on use of the SpatioTemporal Asset Catalogs (STAC) standard which means:
• The search in the dataset is done according to the STAC protocol;
• The Federated Catalog search proxy component converts STAC queries into queries adapted to the underlying catalog and returns the results to the user in STAC format;
• The services are presented in service catalog.
Edge Services offered by DEDL provides:
• Cloud Computing
• STACK Application Development Environment
• Hook Services
The cloud computing service is powered by the ISLET infrastructure, a distributed Infrastructure as a Service (IaaS) built on OpenStack, using the Horizon interface. It allows users to manage virtual machines, s3 storage, and run advanced computations via a graphical user interface (GUI) or command-line interface (CLI). For more complex tasks, Kubernetes integration is available. A standout feature of ISLET is its proximity to data sources, operating near High-Performance Computing (HPC) facilities. This is achieved through data bridges, enabling efficient processing of large datasets, including those from Digital Twins, in conjunction with HPC systems.
The STACK environment supports application development using JupyterHub and DASK, with Python, and R languages. Users can create DASK clusters on selected infrastructure or cloud sites to process data directly where it resides, removing the need for extensive local setup and optimization.
Hook Services is a set of pre-defined workflows which could be used by users as a ready-to-use processors, e. g. : Sentinel-2: MAJA Atmospheric Correction; , Sentinel-2: SNAP-Biophysical; Sentinel-1: Terrain-corrected backscatter. It also enables workflow functions to generate on-demand higher-level products, such as temporal composites.
The DestinE Data Lake is a transformative initiative that revolutionizes how Earth Observation data is managed and utilized. By integrating innovative infrastructure (ISLET), data services (HDA), reliable processors (Hook Services), and user-friendly development tools (STACK), DEDL enables unprecedented levels of data harmonization, federation, and processing. Moreover, the DEDL plays a crucial role in empowering DestinE users by providing them with seamless access to vast datasets and advanced computational tools. It simplifies the process of data exploration, integration, and analysis, enabling researchers, policymakers, and developers to focus on innovation and decision-making rather than technical barriers. By offering a comprehensive suite of services designed to work close to the data, DEDL ensures that users can efficiently utilize the wealth of information generated by the Digital Twins and maximize the impact of their work. This cutting-edge system enhances climate research capabilities and supports sustainable development efforts on a scale previously unattainable.</abstract>
<slug>foss4g-europe-2025-3279-how-data-lake-services-support-destination-earth-users-a-year-of-insights-and-experiences</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/SEMYHF/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/SEMYHF/feedback/</feedback_url>
</event>
<event guid="eae58139-693a-5e5a-8eb7-77ccb2521507" id="3387">
<room>EL11</room>
<title>Eometadatatool - The Ultimate Stactool</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>This work presents the tool used to create the STAC Copernicus Data Space Ecosystem catalog—the largest and most comprehensive STAC catalog in terms of metadata globally. It details the process from developing a metadata model for Sentinel data, through efficient indexing based on the original metadata files accompanying the products, to result validation and backend system ingestion. A particular highlight is that this entire process is executed using a single tool, eometadatatool, initially developed by DLR, further enhanced, and released as open-source software by the CloudFerro team. Eometadatatool facilitates metadata extraction from the original files accompanying Copernicus program products and others (e.g., Landsat, Copernicus Contributing Missions) based on a CSV file containing the metadata name, the name of the file in which it occurs, and the path to the key within the file. By default, the tool supports product access via S3 resources, configurable through environment variables. The CDSE repository operates as an S3 resource, offering users free access. The tool is aimed to be released as open source in Q1 of 2025. The work will explore potential use cases and demonstrate the basic capabilities of the tool.</abstract>
<slug>foss4g-europe-2025-3387-eometadatatool-the-ultimate-stactool</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DARLAU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DARLAU/feedback/</feedback_url>
</event>
<event guid="c51df008-29fd-5c08-b5f2-cd0c5091bcba" id="3382">
<room>EL11</room>
<title>Use of OSS to power the FBiH spatial data infrastructure</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>The Spatial Data Infrastructure of the Federation of Bosnia and Herzegovina (IPP FBiH) has significantly advanced in recent years, leveraging open-source software (OSS) to enhance data sharing, visualization, and analysis. This paper presents the current state of IPP FBiH, emphasizing the availability of spatial datasets and the role of OSS in their distribution and management. Key initiatives have equipped and trained various institutions to utilize OSS solutions for spatial data infrastructure, including PostgreSQL/PostGIS for database management, GeoServer for web-based data sharing, QGIS for analysis, visualization, and editing, and HALE Studio for data transformation and migration.
Through targeted capacity-building efforts, training programs have been conducted for hundreds of public sector professionals, ensuring the effective adoption of OSS tools in daily workflows. These programs have focused on database management, spatial data publishing, geospatial analysis, and data interoperability. The paper also explores the benefits of OSS implementation, such as cost efficiency, technological independence, and enhanced collaboration between institutions. By examining real-world applications and success stories, this study highlights the transformative impact of OSS on IPP FBiH and provides recommendations for further strengthening the spatial data ecosystem through open solutions.</abstract>
<slug>foss4g-europe-2025-3382-use-of-oss-to-power-the-fbih-spatial-data-infrastructure</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/GVQWXA/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/GVQWXA/feedback/</feedback_url>
</event>
<event guid="631a4065-303e-59db-a202-f4bfe9779621" id="3492">
<room>EL11</room>
<title>Achieving INSPIRE compliance using open-source technologies</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Awareness on benefits of spatial data sharing and achieving their interoperability has been developing for many years. Although the INSPIRE Directive, adopted back in 2007, set legislative guidelines for implementation in EU member states, few could have predicted at the time in which direction technological support for specific SDI implementations would develop. In line with the ideas promoted by INSPIRE, the community gathered around open-source technologies quickly began to respond to legislative requirements by implementing various solutions in this domain.
The Talk will demonstrate the possibilities of integrating and utilizing many open-source projects throughout the entire spatial data lifecycle: storing the spatial data in PostGIS database, maintaining it using OpenLayers web GIS client, harmonizing data models through the transformation and harmonization processes, and serving metadata and INSPIRE-compliant datasets and services using GeoNetwork and GeoServer. Given the complexity of the entire process, an integrated solution ENGEON that simplifies the process and is based entirely on open-source technologies will be presented.</abstract>
<slug>foss4g-europe-2025-3492-achieving-inspire-compliance-using-open-source-technologies</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/XW8AS3/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/XW8AS3/feedback/</feedback_url>
</event>
</room>
<room name="SA01" guid="b2c2c705-1015-5f82-b5c4-38f381b9eb7c">
<event guid="137a699c-a510-5180-bb47-aeb117b7f965" id="3171">
<room>SA01</room>
<title>Multiuser mapping environment with geospatial data version control based on QGIS and NextGIS Web</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>The most popular open-source desktop GIS platform, QGIS (https://qgis.org/), offers rich functionality in mapping, geospatial data editing, and symbolization, making it one of the industry standards. However, collaborative work remains challenging, particularly in server-side data storage with seamless desktop synchronization, conflict resolution of edits from different users, and tracking data changes history.
We develop a set of open-source components to provide a smooth process for simultaneous multi-user data editing from QGIS and web maps. NextGIS Web (https://github.com/nextgis/nextgisweb) is a Web GIS framework with extensive capabilities in storing and publishing geospatial data, including an advanced user permissions management system and built-in data version control for vector layers. It uses QGIS as a renderer, ensuring almost full support of QGIS symbology. NextGIS Connect (https://github.com/nextgis/nextgis_connect) is a QGIS plugin that provides seamless integration between QGIS and NextGIS Web, enabling publishing QGIS projects as web maps, connecting to web maps as QGIS projects, direct server data editing from QGIS, and interactive conflict resolution.
In this talk, we will discuss the current state of the open-source ecosystem "NextGIS Web / NextGIS Connect / QGIS," demonstrate the mechanics that provide seamless multiuser data editing from QGIS, and focus on how the underlying geospatial data version control works, from its organization in the database to the end-users' ability to track data changes.</abstract>
<slug>foss4g-europe-2025-3171-multiuser-mapping-environment-with-geospatial-data-version-control-based-on-qgis-and-nextgis-web</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/EAMSMA/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/EAMSMA/feedback/</feedback_url>
</event>
<event guid="2c51805d-7441-5b6c-84c1-98c6ed8d7e83" id="3490">
<room>SA01</room>
<title>State of GeoTools, JTS and ImageN</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>The GeoNetwork-opensource project is a catalog application facilitating the discovery of resources within any local, regional, national or global "Spatial Data Infrastructure" (SDI). GeoNetwork is an established technology - recognized as an OSGeo Project and a member of the foss4g community for over a decade.
The GeoNetwork team would love to share what we have been up to in 2022!
The GeoNetwork team is excited to talk about the different projects that have contributed with the new features added to the software during the last twelve months. Our rich ecosystem of schema plugins continues to improve; with national teams pouring fixes, improvements and new features into the core application.
We will also talk a bit about the health and happiness of the GeoNetwork opensource team. Progress of our main branches (3.12.x and 4.0.x), and release schedule.
Attend this presentation for the latest from the GeoNetwork community and this vibrant technology platform.</abstract>
<slug>foss4g-europe-2025-3490-state-of-geotools-jts-and-imagen</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/TZ7LQD/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/TZ7LQD/feedback/</feedback_url>
</event>
<event guid="47e31a3d-0b06-5342-9b09-de5b6285ec78" id="4142">
<room>SA01</room>
<title>State of GRASS</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Join us for a lively overview of the current state of the GRASS project, where community meets cutting-edge geospatial technology. Whether you're a longtime power user or a newcomer curious about GRASS, this talk will highlight the major strides the project has made in the past year – from revitalized governance and community growth to technical breakthroughs – and offer a glimpse into what's next.
During the talk, we will address how GRASS has strengthened its governance and support structure by bringing in new members to bolster sustainable leadership and new fiscal sponsorship with NumFOCUS. We will also review GRASS community-building initiatives, such as the NSF-backed efforts that allowed GRASS to establish a mentoring program for new contributors, support our Student Grant program, and hold the GRASS Developer Summit 2025 in Raleigh, NC. We will highlight this past summer's Google Summer of Code project, which demonstrates how community mentoring feeds innovation.
The talk will also address GRASS's new logo and branding initiative over the past year, aiming to give the project a modern look while keeping its iconic elements. Notably, "GRASS GIS" is now officially just GRASS – a simpler name that the community has used colloquially for years. To celebrate, the team launched an online swag shop with GRASS-themed apparel, stickers, and more. We will also look at recent strides in community outreach and learning resources, such as a new tutorial website and the modernization of GRASS's documentation platform.
On the development side, we will show off what the GRASS development team has been hard at work delivering in terms of new features, improved performance, and better integration as part of GRASS 8.5. Under the hood, the team made significant code quality and security improvements, fixing issues flagged by automated linters and code scanners. These efforts pave the way for stricter continuous integration checks and a more robust codebase. The build system is also being modernized: GRASS is transitioning to CMake for easier compilation and maintenance, and an official Conda package is on the way, simplifying installation for Python/R data scientists and lowering entry barriers.
As we celebrate these achievements, we're also looking ahead. The GRASS roadmap outlines ambitious goals for the next few years. We plan to maintain annual releases (GRASS 8.6 is already on the horizon for 2026) and continue improving distribution and integration – think one-click installs via Conda, tighter bridges to QGIS and R, and refined Python and R APIs for smooth scripting. Sustainability remains a core focus: the project actively pursues new grants, sponsors, and community donations to ensure long-term development while spreading infrastructure knowledge and lowering maintenance overhead to avoid burnout.
In short, the state of GRASS is strong and dynamic. This talk will offer an informative yet exciting tour of the project's recent milestones across community and technology. We invite everyone – from newbies to veteran developers – to see how far GRASS has come and to get inspired about where it's heading. Learn about the latest capabilities, meet the people behind the project, and discover how you can be part of the next chapter of GRASS!</abstract>
<slug>foss4g-europe-2025-4142-state-of-grass</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/C89KXN/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/C89KXN/feedback/</feedback_url>
</event>
<event guid="9538b517-9007-5161-a78f-7f6a88607171" id="3491">
<room>SA01</room>
<title>Approaching Security with Kindness and Compassion</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>Wow it has been a busy time for security vulnerabilities. FOSS4G software is getting caught up in the general push to regulate IT and impose “security” on the technology that powers society.
This talk explores the tensions, expectations, terrors and triumphs on this hot button topic. We will look at a sensible response to Europe's Cyber Resilience Act and how GeoSever and GeoNetwork policies have been updated to address these concerns for developers, participating organizations and members of the public.
This talk unpacks what this can look like for foss4g projects using real world examples.
* Built around the experience of the GeoServer project, and the resulting security policy and practices that can serve as a template for our foss4g community.
* Public institutions can attend this talk to learn how their security policies interact with and affect foss4g technologies.
* Vendors and service providers can learn how open source supply chains affect their products.
* FOSS4G projects can attend to learn how to approach security reports with compassion, and a bit of boundary setting, to take care of your codebase and community.
Security is difficult with consequences being felt at all levels. Help meet this challenge by supporting yourself and each other.</abstract>
<slug>foss4g-europe-2025-3491-approaching-security-with-kindness-and-compassion</slug>
<track>Open community</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DHSDKX/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DHSDKX/feedback/</feedback_url>
</event>
<event guid="3c0a83ca-def8-5154-9885-c9d878cc49a6" id="3391">
<room>SA01</room>
<title>Community led Open Mapping Solutions: addressing humanitarian needs from start to finish</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>At the Humanitarian OpenStreetMap Team (HOT), we envision an ecosystem of open mapping technology that enables everyone, and in particular vulnerable communities, to make open map data available in order to use in disaster response and humanitarian context. We focus on building with our community and involving our users in every step of the process.
In this session, we would like to take you on a journey in introducing the full end-to-end open mapping workflow, from collecting data to generating insights. From gathering aerial imagery with UAVs, remote mapping, AI assisted mapping to field data collection and then downloading and using the map in disasters and humanitarian work. You will hear about the key open source tools enabling that process - from the newly developed Drone Tasking Manager to fAIr (our AI assisted mapping service), Field Mapping Tasking Manager and HOT Export tool. We will share some stories from case studies in testing the end to end mapping workflow in Indonesia, Nepal, Sierra Leone and the lessons learnt.
We hope that you will leave this talk inspired and with an understanding on how YOU can become part of the end to end mapping journey!
HOTOSM website: https://www.hotosm.org/tech-suite
HOTOSM Github: https://github.com/hotosm</abstract>
<slug>foss4g-europe-2025-3391-community-led-open-mapping-solutions-addressing-humanitarian-needs-from-start-to-finish</slug>
<track>Open community</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8A7UMZ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8A7UMZ/feedback/</feedback_url>
</event>
<event guid="a0ee8bde-ae31-52cc-abe4-4b7166310a4a" id="3518">
<room>SA01</room>
<title>The Importance of FOSS4G and Open Geospatial data for agricultural land use planning</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:05</duration>
<abstract>Public Institution Agricultural Institute of Republic of Srpska Banja Luka (JU PIRS) is a scientific institution founded in 1947. The basic responsibilities, mission and goals of the JU PIRS are research activities in the field of agricultural development (with the focus on general interest), i.e. affairs that have strategic importance for Republic of Srpska (one of the entities in Bosnia and Herzegovina). These activities represent the basis for effective and professional assistance to agricultural producers and improvement of the overall agricultural production in the Republic of Srpska. A very important role of the JU PIRS is the transfer of new scientific and technological achievements to all interested groups. The activities of the Institute are defined through the work of nine Departments. One of them is the Department of the Agroecology. The core of this Department is research and application of the results in practice within the fields of: Soil, Agrochemistry, Plant nutrition and Agroecology. The overall goal of the research is the improvement of Soil fertility (by achieving high and stable yields-in terms of quality and quantity) with the coherency to good agricultural practices and the protection of the Environment.
The Department of Agroecology in addition to the research in the field of proper nutrition of agricultural breeds and protection of human environment, since 2000 applies GIS. Due to the challenges in the development of the Department/JU PIRS and the rapid growth of the FOSS4G solutions/Free and Open Geospatial data, the Department of Agroecology has embraced those technologies and applied them in everyday work.
According to the Law on Agricultural Land of the Republic of Srpska, the entity itself, municipalities and cities are obliged to prepare a planning document “Groundwork for Agricultural Land Protection, Use and Restructuring (The groundwork)”. JU PIRS, i.e. the Department of Agroecology is legit to do such a job. Information related to the current state of land cover and land use are essential for The groundwork. The Department of Agriculture uses FOSS4G and Free and Open Geospatial Data to gather, analyze, process/elaborate and visualize land cover and land use data. The basic idea is to distinguish what is agricultural land and what belongs to non-agricultural land. Practically speaking, the following solutions are used:
1. The OpenStreetMap is used to distinguish the boundaries of the land covered with forest and water-bodies.
2. QGIS is used to visualize all geospatial data related to land cover and land use, to edit and delineate classes. Besides, the QGIS (with the GRASS GIS and SAGA GIS) is applied to process data related to soil fertility and agricultural land contamination.
3. Sentinel-2 data (with combination of available ortho-photo imagery) is applied in the process of the delineation of land cover land use classes.
Besides the application of FOSS4G solution in everyday job, the Department of Agroecology is transferring the knowledge to the final users using same technologies.</abstract>
<slug>foss4g-europe-2025-3518-the-importance-of-foss4g-and-open-geospatial-data-for-agricultural-land-use-planning</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VJKMVM/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VJKMVM/feedback/</feedback_url>
</event>
<event guid="fba37bea-74f1-5d4f-9fc3-f307bc773c7a" id="3308">
<room>SA01</room>
<title>State of OWSLib</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:35:00+02:00</date>
<start>14:35</start>
<duration>00:05</duration>
<abstract>OWSLib is a Python package for client programming with Open Geospatial Consortium (OGC) web services. The project has had regular releases since 2006, and is listed as a dependency by over 3,000 projects on GitHub, and has over 6,000 daily downloads.
This talk gives a quick update on new features in the project, support for the new OGC APIs, and the future roadmap.</abstract>
<slug>foss4g-europe-2025-3308-state-of-owslib</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/BLXNJG/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/BLXNJG/feedback/</feedback_url>
</event>
<event guid="36ef2ca6-a917-5639-b9b1-c2e5a5142301" id="3283">
<room>SA01</room>
<title>QGIS2Mapbender QGIS Plugin - Publish your QGIS Project directly in Mapbender</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:40:00+02:00</date>
<start>14:40</start>
<duration>00:05</duration>
<abstract>QGIS2Mapbender is a brand new plugin that allows you to publish a QGIS project via QGIS Server and add it to a Mapbender application with just a few clicks in a plugin in your QGIS Desktop.
Cool solution. See how it works.</abstract>
<slug>foss4g-europe-2025-3283-qgis2mapbender-qgis-plugin-publish-your-qgis-project-directly-in-mapbender</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8GDETM/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8GDETM/feedback/</feedback_url>
</event>
<event guid="62a5fcdd-1a29-52f3-8073-f8abc24a0d1a" id="3411">
<room>SA01</room>
<title>Lost in Processing: Making Sense of Imagery Processing Levels</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:45:00+02:00</date>
<start>14:45</start>
<duration>00:05</duration>
<abstract>**“If I Had More Time, I Would Have Written a Shorter Letter”**
A (short) talk about our struggles at UP42 to make sense of a constellation of optical processing level standards, while highlighting the importance of effectively distilling and communicating these concepts to a less technical audience.</abstract>
<slug>foss4g-europe-2025-3411-lost-in-processing-making-sense-of-imagery-processing-levels</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/HGGATJ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/HGGATJ/feedback/</feedback_url>
</event>
<event guid="484f149a-b9ae-5bbd-8b2c-4b3c61383500" id="3425">
<room>SA01</room>
<title>Deploying GeoNode with Kubernetes: Introducing GeoNode-K8s</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:50:00+02:00</date>
<start>14:50</start>
<duration>00:05</duration>
<abstract>In the realm of geospatial data management, GeoNode stands out as a robust open-source platform [1]. However, deploying and scaling GeoNode can present challenges, especially in dynamic environments. To address these challenges, the German GeoNode User Group has developed and maintains GeoNode-K8s, a Kubernetes Helm chart tailored for GeoNode deployments [2]. This tool streamlines the deployment process, offering scalability and resilience through Kubernetes orchestration. In this lightning talk, we will:
Introducing GeoNode-K8s and its core features.
Demonstrate the deployment process using the Helm chart.
Discuss the advantages of leveraging Kubernetes for GeoNode, including improved scalability and simplified management.
Attendees will gain insights into how GeoNode-K8s can enhance their geospatial data infrastructure, making deployments more efficient and adaptable to varying workloads.
This talk is designed for geospatial professionals and developers interested in modernizing their GeoNode deployments using container orchestration technologies.
References:
[1] https://github.com/geonode/geonode - GeoNode Official Repository
[2] https://github.com/GeoNodeUserGroup-DE/geonode-k8s - GeoNode-K8s GitHub Repository</abstract>
<slug>foss4g-europe-2025-3425-deploying-geonode-with-kubernetes-introducing-geonode-k8s</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/SWDRHL/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/SWDRHL/feedback/</feedback_url>
</event>
<event guid="aed3fa31-2504-59b3-857e-d3c861ebd5b2" id="3115">
<room>SA01</room>
<title>Mercator is your friend</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Mercator projection is nowadays hated by many people. A movement started (at least) in the 1970's by Arno Peters. However it has unique properties that other projections miss. Those properties are useful not only in surveying (big scales), but also in worldwide data representation (small scales).
This talk will explain the pros and cons of the Mercator projection and compare it to other projections. It will explain how to measure distances in any projection. I hope it will give hints to decide which projection to use depending on the purpose of the map.</abstract>
<slug>foss4g-europe-2025-3115-mercator-is-your-friend</slug>
<track>FOSS4G in education and research</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FD9YYJ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FD9YYJ/feedback/</feedback_url>
</event>
<event guid="37ba53fa-f30f-5948-83e5-9704ac4ed1b2" id="3461">
<room>SA01</room>
<title>The shape of GIS to come</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Mankind has been flattening the Earth for more than two thousand years to represent and process geo-spatial data. This practice has imposed relevant penalties in accuracy with the distortions inherent to map projections. An holistic approach to global geo-spatial was never really tackled.
But a new era is dawning. Computer Systems are finally brewing the impact on geo-spatial information the were always meant to have. As geo-spatial data are no longer stored on flat sheets, the distortions and liabilities of traditional cartography can be addressed.
Since 1990 researchers and computer programmers have worked on methods and models to partition the surface of the Earth, paving the way for a modern representation of geo-spatial data. Broadly known as Discrete Global Grid Systems (DGGS) these models map any location on the surface to both a unique logical index and a geodetical zone. These zones form a regular or quasi-regular partition of the Earth's surface in its entirety, and may be created at different sizes or scales (also known as grid resolutions).
In recent years the number of institutions and enterprises adopting or developing DGGSs has erupted, leading to numerous tools and implementation, progressively approaching the expectations of regular GIS users. Moreover, members of the OGC have developed two standards, an abstract DGGS specification and just this year a data provision API. These developments usher a new era, in which most users will have some means of working with DGSSs.
This address presents in first place a brief history of DGGS. It then lays out the current spectrum of DGGS tools and their applications. It closes with notes on expected developments in the near future.</abstract>
<slug>foss4g-europe-2025-3461-the-shape-of-gis-to-come</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/JFCK3B/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/JFCK3B/feedback/</feedback_url>
</event>
<event guid="246e5e14-ed2b-58cc-b1c4-dbeba3f2ef18" id="3517">
<room>SA01</room>
<title>Standardisation of Climate Services Information Systems</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>A number of policy initiatives over the last three decades have been striving to limit the impacts of global warming, reverse land degradation, stop the loss of biodiversity, protect finite natural resources, and reduce the risks associated with environmental disasters. They are summarized under the umbrella
of the United Nations Agenda 2030 and the Sustainable Development Goals (SDGs), which together aim at building the resilience of people and systems to new environmental and socioeconomic conditions in the future.
Beyond political will, achieving these objectives and tracking progress requires science, data and technology: sophisticated observation instruments and networks, coordinated modeling experiments, scientific assessments, global data access infrastructures and analytical capacity to process
and translate data into relevant information. While some of these endeavors have benefited from international coordination and funding under the umbrella of the UN 2030 Agenda for Sustainable Development, the Paris Agreement on climate change, the Sendai Framework for Disaster Risk Reduction and other framework agreements, many still rely on uncoordinated national or international projects, resulting in a proliferation of incompatible, short-lived initiatives. Beyond the waste of time and energy, our main concern is that many solo technological initiatives all solve the same simple problems, deferring work on more complex issues.
Advances on the hard global problems of our time requires greater collaboration and innovation. We argue that policy instruments should include technological guidelines, for example mandate the use of international standards for analysis ready data formats, metadata, geodatacubes or machine to machine communication protocols, in order to foster interoperability and software reuse. This would strengthen
international collaboration on innovative development, and in turn contribute to the deployment of effective, robust and scientifically credible products to support decision-making and
local climate action.</abstract>
<slug>foss4g-europe-2025-3517-standardisation-of-climate-services-information-systems</slug>
<track>Open standards and interoperability for geospatial</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/PJZR9N/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/PJZR9N/feedback/</feedback_url>
</event>
</room>
<room name="SA02" guid="65085993-f6f2-5613-95e2-cda29c59e1c5">
<event guid="ace005bf-b16d-5c01-a194-8ea455bef4bf" id="3497">
<room>SA02</room>
<title>Revisiting earning your support 10 years on</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>10 years ago I gave a [talk](https://vimeo.com/144089061) at FOSS4G suggesting that if you wanted to get a "good" response from open source developers that you should try to bank some social capital first. For the next 10 years every message sent to the GeoServer user list (now defunct) had a link to that talk in it's signature. Did that make any difference?
This talk will look back on the past decade and look at the changes that have occurred in the open source community in general and in FOSS4G in particular. Many more companies and governments have jumped into using open source software as it is "free" in the gratis sense. In many cases they have not considered that "free software" is worth exactly what you pay for it. Instead they continue to show a remarkable sense of entitlement, demanding security based on automated scanning with out offering any help.
It has been suggested to me that "not everyone can be a programmer" so it is unreasonable to expect people to contribute to fixes for their software, but there are many other ways to contribute to a project such as documentation fixes, tutorials, web site design and many other things that they can do.</abstract>
<slug>foss4g-europe-2025-3497-revisiting-earning-your-support-10-years-on</slug>
<track>Open community</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/3BRPRG/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/3BRPRG/feedback/</feedback_url>
</event>
<event guid="2bf0a902-2388-5399-94b1-7c0c755fa577" id="3486">
<room>SA02</room>
<title>Scaling FOSS Development and Decisionmaking feat MapLibre Native</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>Your FOSS project is useful to people, and it gets integrated in more and more systems by upstarts, hobbyists, large corporations, and everything in between. Inevitably this will lead to bug reports as well as wishes for new features from your growing community. Some users will start to participate in discussions, others will also start to make real investments into development. More users, more wishes, more development. At some point you will need to start thinking about how to scale the development efforts and decisionmaking process.
MapLibre Native is a map rendering toolkit fortunate enough to have this problem. The maintainer will share how we are dealing with this challenge so far and the ways we still have to go. What are the (sometimes conflicting) needs of our diverse user base? How do we come together to drive innovations, such as support for the new MapLibre Tile format, 3D Models and non-Western writing systems?</abstract>
<slug>foss4g-europe-2025-3486-scaling-foss-development-and-decisionmaking-feat-maplibre-native</slug>
<track>Open community</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/WMYSPU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/WMYSPU/feedback/</feedback_url>
</event>
<event guid="3fff6081-b8bd-5fc7-b0fb-8356637f1753" id="3421">
<room>SA02</room>
<title>From Challenges to Achievements: The Collaborative Path of the German GeoNode User Group</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>The German GeoNode User Group is an inclusive community that welcomes participants from over ten research institutions and multiple companies, all united by a shared interest in GeoNode, a web-based platform for developing geospatial information systems. Our group operates on a flexible basis, allowing members to contribute to discussions and projects in accordance with their current work capacities and level of involvement with GeoNode [1].
We maintain regular communication through our GitHub repository, which serves as the central hub for collaboration, code sharing, and project coordination. This is complemented by a dedicated Slack channel that facilitates direct and efficient interactions among members. Our meetings, held four times a year with at least one in-person gathering during the German FOSSGIS conference, provide opportunities to discuss individual challenges, share updates from the GeoNode upstream community, and exchange news about our respective GeoNode instances.
A significant challenge we've addressed is the exchange of GeoNode features that haven't been integrated into the upstream project and sharing them among our individual forks. This led to the development of the geonode-blueprint-docker [2], an opinionated setup designed to simplify GeoNode installations and configurations, making it more accessible for our diverse community [3].
Our collaborative efforts have resulted in several notable projects:
- Thünen Atlas: An interactive platform providing maps and data on topics such as land use, forestry, and marine ecosystems [4]
- Data Package Contrib Module: A GeoNode importer handler designed to upload non-spatial data as data packages, enhancing the platform's versatility [5]
- GeoNode-K8s: A Kubernetes Helm chart facilitating the deployment of GeoNode in cloud-native environments, promoting scalability and ease of management. [6]
Our GitHub repository serves also as a hub for these open-source GeoNode extensions and ecosystem tools, such as geonodectl, a command-line interface tool for interacting with GeoNode's REST API v2.
Beyond our internal projects, we actively engage with the upstream GeoNode community by providing feedback on feature development and contributing both bug reports and new features to the core GeoNode platform. Notably, one of our members serves on the GeoNode Project Steering Committee, further strengthening our connection with the broader GeoNode community.
Through this presentation, we aim to showcase our community's achievements and extend an invitation to new members to join our collaborative efforts. By sharing our experiences, we hope to inspire others to participate in and benefit from our collective endeavors.
[1] https://geonode.org - GeoNode Official Website: GeoNode is an open-source platform that facilitates the creation, sharing, and collaborative use of geospatial data.
[2] https://github.com/GeoNodeUserGroup-DE/geonode-blueprint-docker - GeoNode-Blueprint-Docker: This is a Docker blueprint for setting up a GeoNode installation, simplifying the deployment process for users.
[3] https://github.com/GeoNodeUserGroup-DE/ - German GeoNode User Group GitHub Repository: The central hub for the German GeoNode User Group's projects and collaborations, hosting various tools and extensions related to GeoNode.
[4] https://atlas.thuenen.de - Thünen Atlas: An interactive platform providing maps and data on topics such as land use, forestry, and marine ecosystems.
[5] https://github.com/GeoNodeUserGroup-DE/contrib_datapackage - Contrib DataPackage: A GeoNode importer handler designed to upload non-spatial data as data packages, enhancing the platform's versatility.
[6] https://github.com/GeoNodeUserGroup-DE/geonode-k8s - GeoNode-K8s: A Kubernetes Helm chart facilitating the deployment of GeoNode in cloud-native environments, promoting scalability and ease of management.</abstract>
<slug>foss4g-europe-2025-3421-from-challenges-to-achievements-the-collaborative-path-of-the-german-geonode-user-group</slug>
<track>Open community</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/PWKE9P/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/PWKE9P/feedback/</feedback_url>
</event>
<event guid="0113e369-4193-52b6-a757-a5f0854da1dc" id="3422">
<room>SA02</room>
<title>BSEQ: A new approach to draught managing</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>BSEQ originates from the need to provide the local government with a simple visualization tool to monitor water consumption per inhabitant across different drought scenarios.
During times of water scarcity, it is more crucial than ever to have a GIS system that displays an inventory of network assets, enables hydraulic balance calculations, identifies leaks, and simulates consumption scenarios. In addition to these basic functions, GIS combined with PostGIS facilitates large-scale data processing, aiding decision-making in real-world situations. Based on QGIS, with its Giswater extension and PostgreSQL integration, we already have a specialized GIS for managing potable water supply and sewage networks. Therefore, analysing drought-related parameters is a logical progression toward improving water cycle management—an increasingly urgent issue in Spain, particularly in Andalusia and Catalonia. In these regions, we are actively collaborating with local administrations to mitigate the effects of this unprecedented drought. Unfortunately, the information provided by GIS does not always reach government teams clearly, as processing the data requires significant time and technical expertise.
This is precisely why we developed BSEQ—an automated solution designed to address this challenge, recognizing that local administrations often lack the human resources to carry out such data analysis. By cross-referencing cadastral records, municipal registers, and individual water consumption data—including details such as the square meters of gardens and swimming pools on each property—BSEQ makes it easier to identify areas with high water usage potential. As a secondary benefit, this data integration also helps detect second homes, irregularities in the municipal register, and possible unauthorized occupations.
BSEQ features a reporting system powered by the open-source technology Apache Superset, which allows for quick and intuitive graphical analysis of results. This enhances decision-making while also providing the option to export data for further processing with other applications. The platform's web-based environment ensures easy access and transparency, as it can be viewed from any browser with an internet connection.
Our philosophy is built on three core principles: simplicity, speed, and affordability. Implementing BSEQ in a corporate environment requires only the aforementioned data, which all municipalities should have easy access to. Within a maximum of two weeks, the system can process this data and be fully operational.</abstract>
<slug>foss4g-europe-2025-3422-bseq-a-new-approach-to-draught-managing</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/9CNP7Q/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/9CNP7Q/feedback/</feedback_url>
</event>
<event guid="385b16b5-fcc0-5f43-b14f-32315a684527" id="3424">
<room>SA02</room>
<title>DRAIN: QGIS Plugin for 1D/2D hydraulic modelling</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>Proprietary software capable of performing 1D/2D hydraulic modeling often presents a significant financial barrier for individuals, researchers, and public institutions, limiting accessibility and hindering hydrological studies. To overcome this challenge, we have developed Digital RAIN (DRAIN), an open-source QGIS plugin designed to facilitate 1D/2D hydraulic modeling in urban environments.
DRAIN leverages the combined strengths of EPA SWMM and Iber, two widely used hydraulic modeling tools, to simulate rainfall events and their impact on drainage systems. The tool provides detailed simulation results, including calculated hydrodynamic parameters for each element of the hydraulic infrastructure (processed by SWMM) and high-resolution raster outputs that depict accumulated rainfall and surface flow over time (generated by Iber).
At the core of DRAIN, a GeoPackage-based data model ensures seamless integration between Iber and SWMM, incorporating triggers and constraints to maintain data consistency and accuracy. The plugin also features an intuitive graphical interface with interactive GIS tools, allowing users to efficiently manage spatial data, define modeling parameters, and execute simulations directly within the QGIS environment.
By offering a free, open-source alternative to proprietary hydraulic modeling software, DRAIN promotes accessibility, reproducibility, and innovation in urban hydrology studies. This tool has the potential to support decision-making processes related to flood risk management, stormwater infrastructure design, and climate resilience planning in urban areas.</abstract>
<slug>foss4g-europe-2025-3424-drain-qgis-plugin-for-1d-2d-hydraulic-modelling</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/9C8SHN/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/9C8SHN/feedback/</feedback_url>
</event>
<event guid="f3d72a9d-fd01-5771-91fe-f60a9524c197" id="3502">
<room>SA02</room>
<title>Adapting Legacy Code with Boost.Geometry: A Hydrographic Perspective</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>QPS maintains a substantial legacy codebase of approximately 5.5 million lines of code, developed over the past 35 years to collect, process, analyze, and deliver hydrographic spatial data. Updating this codebase to incorporate new libraries often requires significant refactoring to accommodate new data types.
Recently, we successfully transitioned from an in-house solution to `Boost.Geometry` for spatial indexing. This transition required far less refactoring than anticipated, primarily due to `Boost.Geometry`'s ability to adapt to our existing geometry types.
`Boost.Geometry`'s geometry types are concrete implementations of "concepts," which facilitate the adaptation of user-defined geometry types to seamlessly integrate with its algorithms and spatial indexing features.
In this presentation, I will:
* Provide an overview of the unique spatial data we handle in hydrography.
* Share our success story of transitioning to `Boost.Geometry`.
* Discuss how `Boost.Geometry` enabled us to generalize our spatial indexing.
* Demonstrate, through a brief code example, how to use concepts to adapt existing types for use with `Boost.Geometry`.
Join me to learn how `Boost.Geometry` can streamline your transition to modern spatial indexing solutions while minimizing codebase disruption.</abstract>
<slug>foss4g-europe-2025-3502-adapting-legacy-code-with-boost-geometry-a-hydrographic-perspective</slug>
<track>Transition to FOSS4G</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/C8EMH7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/C8EMH7/feedback/</feedback_url>
</event>
<event guid="c2c8a323-ad4a-59c5-813b-706d4264afd6" id="3439">
<room>SA02</room>
<title>FLUENT: prediction of pipe leaks using IA, LEYP and log regression. Data pipeline using PostgreSQL</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Pipe leaks are a significant concern for water companies responsible for managing water infrastructures. In this context, anticipating these events is crucial not only for conserving water but also for ensuring that the infrastructure remains in optimal condition.
The FLUENT project presents a unique opportunity for water companies to study the probability of pipe leaks using artificial intelligence (AI), Linear Extended Yule Process (LEYP) , and logistic regression (LR) enabling them to predict and proactively address potential issues. The main goal of the project is to develop a predictive system for pipe leaks using advanced AI algorithms. To achieve this, four water companies, serving between 20,000 and 100,000 consumers, contributed their data to train the AI model. These companies also collaborated to establish common definitions of key concepts and to share valuable knowledge on how to tackle the challenges associated with leak detection and prevention.
The collected data was stored in a PostgreSQL database, and was processed using PL/pgSQL and PostGIS functions, allowing for efficient data manipulation and preparation before being used by the AI algorithms outside the database. This collaborative approach not only aims to improve the accuracy of leak predictions but also seeks to provide practical solutions to enhance infrastructure management and promote more sustainable water usage practices. By leveraging AI in this way, the project strives to advance the capabilities of water companies in addressing one of the most pressing challenges in water distribution networks today.</abstract>
<slug>foss4g-europe-2025-3439-fluent-prediction-of-pipe-leaks-using-ia-leyp-and-log-regression-data-pipeline-using-postgresql</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/3Z8WAQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/3Z8WAQ/feedback/</feedback_url>
</event>
<event guid="4c27b09c-db5b-586d-9693-67a235e3d061" id="3505">
<room>SA02</room>
<title>NaLaMap: An open-source framework to use Large Language Models in WebGIS</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Maps have long been essential tools in both public and private sectors. However, until recently, Geographic Information Systems (GIS) and map creation were largely confined to a small community of highly trained experts.
Mainstream adoption has been stifled by several entry barriers:
* the need for specialized domain knowledge in GIS concepts, data, and methodologies,
* the technological complexity inherent in data management, integration, and processing,
* complex user interfaces that expose too many options (tools and data) &amp;
* prohibitive software licensing costs, especially for public institutions in developing countries.
These challenges have often necessitated hiring specialized staff, thereby limiting the proliferation of geospatial technologies among small to medium enterprises and public agencies, despite the critical role geoinformation plays in addressing key 21st-century challenges such as climate change adaptation.
Recent advancements in Generative Artificial Intelligence, especially the emergence of Large Language Models and Agent-Based Systems promise to lower these barriers significantly. In response, we have initiated the open-source project NaLaMap (Natural Language Mapping).
NaLaMap is designed to democratize access to geospatial information by offering users the possibility to control key parts of the map creation process with natural language commands, including:
- data research,
- geospatial processing,
- geocoding
- layer styling &amp;
- data integration via OGC compliant protocols (WMS/WFS/WMTS)
The framework is built upon widely adopted open-source technologies such as Python (Geopandas, Shapely, Fiona), ReactJS, LangChain, Graphagent, and Leaflet. Its core UI-components include a user-friendly chatbot, an interactive map interface, and a data library based on open-data portals.
NaLaMaps chatbot automatically geocodes user requests and intelligently searches for tools, datasets, and unstructured information, enabling users to generate maps with written text commands and evaluate data suitability for specific data-related questions. This makes it an intuitive tool for your map audience and people who might be overwhelmed by using traditional GIS tools. In addition it can also speed up your map creation process and allows you to play arround with cool features such as intelligent (automated) layer styling.
NaLaMap allows adding custom backends, which enables developers to put it on top of their existing geospatial infrastructures. Furthermore, the system offers the possibility to adapt and extend the GIS-toolbox offered to the agent as well as the prompts that control its behavior and tool usage.
Out talk and open-source software framework is intended for GIS users and developers alike who would try to make their first steps with LLMs and GIS without having to create a complete WebGIS framework from scratch. The purpose of NaLaMap is to increase the reach of geodata products from established teams and projects. In doing so, it has the potential to empower decision-makers across multiple sectors—from urban planning and environmental monitoring to crisis response.
With our talk we would like to invite the FOSS4G community to join us in pioneering a more accessible, efficient, and inclusive future for geospatial intelligence.</abstract>
<slug>foss4g-europe-2025-3505-nalamap-an-open-source-framework-to-use-large-language-models-in-webgis</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/HMTBTP/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/HMTBTP/feedback/</feedback_url>
</event>
<event guid="afd8ef6b-a02c-595b-aee3-97cb5191faca" id="3309">
<room>SA02</room>
<title>State of MapServer</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:05</duration>
<abstract>MapServer is a founding OSGeo project and used for publishing spatial data and interactive mapping applications to the web. This talk provides an overview of enhancements and features in the new 8.4 release of MapServer and its scripting language MapScript, along with upcoming plans for the future.</abstract>
<slug>foss4g-europe-2025-3309-state-of-mapserver</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/EFUSN8/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/EFUSN8/feedback/</feedback_url>
</event>
<event guid="aae76352-02eb-52b6-976d-bb9ad57963b9" id="3509">
<room>SA02</room>
<title>Discover PostGIS Functions with ST_Letters</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T16:35:00+02:00</date>
<start>16:35</start>
<duration>00:05</duration>
<abstract>Get to know the ST_Letters function that allows you to visualize the results of PostGIS functions with letters. ST_Letters renders a string as a multipolygon geometry. It is fun to use the function to experiment with PostGIS functions. Get to knwo how it works.</abstract>
<slug>foss4g-europe-2025-3509-discover-postgis-functions-with-stletters</slug>
<track>Transition to FOSS4G</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/PNYWM8/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/PNYWM8/feedback/</feedback_url>
</event>
<event guid="debd3272-74fa-5dee-b1b5-b97819492b9d" id="3312">
<room>SA02</room>
<title>DestinE DataLake Lab: A Guide to Utilizing Destination Earth Data Lake Services</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T16:40:00+02:00</date>
<start>16:40</start>
<duration>00:05</duration>
<abstract>The Destination Earth Data Lake Lab (DestinE-DataLake-Lab) is a comprehensive GitHub repository designed to facilitate users' interaction with the Destination Earth Data Lake (DEDL) services. Developed by EUMETSAT and partners, this repository offers a collection of Jupyter Notebook examples and Python tools that demonstrate how to effectively utilize various DEDL services, including Harmonized Data Access (HDA), STACK, and HOOK.
Harmonized Data Access (HDA)
The HDA service provides users with streamlined access to a diverse range of datasets within the DEDL ecosystem. Within the repository, the HDA directory contains Jupyter Notebook examples that guide users through the process of discovering available services, listing and searching for STAC collections, and retrieving specific data items. These examples are instrumental in helping users understand how to interact with the HDA API, manage authentication, and perform data queries efficiently.
STACK Service
The STACK service is designed to facilitate near-data processing by leveraging DASK, a flexible parallel computing library in Python. In the STACK directory of the repository, users will find Jupyter Notebook examples that illustrate how to set up and utilize DASK for processing large datasets distributed across different cloud locations. These examples demonstrate the deployment of DASK clusters, execution of parallel computations, and optimization of data processing workflows, enabling users to perform complex analyses efficiently.
HOOK Service
The HOOK service offers Function-as-a-Service (FaaS) capabilities, allowing users to define and execute workflows within the DEDL environment. The HOOK directory in the repository provides Jupyter Notebook examples that guide users through the process of creating, deploying, and managing workflows using the HOOK service. These tutorials cover various aspects, including defining functions, setting up triggers, and monitoring workflow execution, thereby enabling users to automate data processing tasks effectively.
Getting Started
To begin utilizing the resources provided in the DestinE-DataLake-Lab repository, users are encouraged to clone the repository into their local environment or access it through the DEDL-provided JupyterHub - STACK Service. The repository includes a requirements.txt file that lists the necessary Python dependencies. Users should create a virtual environment, install the required packages, and select the appropriate kernel when running the provided notebooks. Detailed instructions for setting up the environment and installing dependencies are available in the repository's README file.
Additional Resources
For further information and comprehensive documentation on DEDL services, users can refer to the DestinE Data Lake documentation. This resource provides in-depth guides, API references, and additional tutorials to assist users in maximizing their utilization of DEDL services. Moreover, the DestinE Data Portfolio and Data Lake Edge services offer valuable insights into the available datasets and services within the DEDL ecosystem.
Summary
In summary, the DestinE-DataLake-Lab repository serves as a valuable resource for users aiming to effectively engage with the Destination Earth Data Lake services. By providing practical examples and comprehensive guides, it empowers users to harness the full potential of DEDL's offerings, facilitating efficient data access, processing, and workflow management within the Destination Earth initiative.</abstract>
<slug>foss4g-europe-2025-3312-destine-datalake-lab-a-guide-to-utilizing-destination-earth-data-lake-services</slug>
<track>FOSS4G in education and research</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/7JYZJH/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/7JYZJH/feedback/</feedback_url>
</event>
<event guid="806c66ad-edb9-5ce4-a500-3d3b22887eea" id="3748">
<room>SA02</room>
<title>Interactive line and polygon flow maps</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T16:45:00+02:00</date>
<start>16:45</start>
<duration>00:05</duration>
<abstract>Well-designed origin-destination flow maps remain difficult to automatically draw, mainly due to the complexity of managing and clearing clutter caused by overlapping flow paths. A few automated methods to create these exist, but most generally allow little, if any, customization of the output. This research presents further functionality and advancements of an existing free and open source, interactive, semi-automated flow mapping script for generating and customizing flow paths for use in geographic information system (GIS) or web map settings. Written in Python, the script accepts a comma-separated values file of origins and destinations along with attributes such as the magnitude of flows, and produces output flow paths. Previously, the script only produced skeletal flow polylines which could then be symbolized in subsequent GIS software packages; the script now also optionally produces polygon arrows suitable for direct loading into, for example, webmaps. Further, an interactive GUI is being developed that allows users to tweak arrow path parameters and see their changes in real time, before writing output to file. The presentation will live demonstrate the software in action, particularly in producing polygon arrows.</abstract>
<slug>foss4g-europe-2025-3748-interactive-line-and-polygon-flow-maps</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VVN3WM/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VVN3WM/feedback/</feedback_url>
</event>
</room>
<room name="CA01" guid="d704391c-14ae-55a0-8038-46cac687da65">
<event guid="25dc1093-85c5-5fab-8bce-d44c295bd3c2" id="3282">
<room>CA01</room>
<title>Create great geoportal applications with Mapbender</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>Mapbender is a great open source solutions for creating intuitive and high-performance WebGIS applications. Mapbender offers a set of tools that you can combine.
This software solution enables users to quickly and easily publish applications online without having to write a single line of code.
Mapbender improved a lot. With the new version we have a refactored design and many new or improved features. You can integrated your WMS Services and confirgure them individually. You can manage access rights for applications.</abstract>
<slug>foss4g-europe-2025-3282-create-great-geoportal-applications-with-mapbender</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/J3LN8J/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/J3LN8J/feedback/</feedback_url>
</event>
<event guid="d487be28-c903-50cc-9829-035de8d3bbc5" id="3442">
<room>CA01</room>
<title>Creating Web-Ready QGIS Plugins: Insights from Giswater for Effective QWC2 Compatibility</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>In the ever-evolving realm of geospatial technology, developing QGIS plugins that are both reliable and adaptable for web environments is essential. This talk will provide practical insights into creating QGIS plugins that perform well across both QGIS desktop and QGIS Web Client (QWC2).
Key areas of focus include:
Database-Driven Design: Explore how a database-driven approach can enhance both the functionality and user interface of QGIS plugins. This method simplifies development by allowing dynamic configuration and management of plugin features and UI elements based on backend data, ensuring seamless integration with various data sources and use cases.
Qt Forms as a Service: Learn how to implement Qt Forms as a service, where forms are dynamically generated and customized according to backend configurations. This approach facilitates the creation of adaptable and maintainable user interfaces that respond efficiently to different data inputs and user needs.
Through the Giswater plugin case study, this talk will showcase these concepts with practical examples. Attendees will gain valuable insights into building web-ready QGIS plugins that are robust, flexible, and user-friendly across diverse platforms.</abstract>
<slug>foss4g-europe-2025-3442-creating-web-ready-qgis-plugins-insights-from-giswater-for-effective-qwc2-compatibility</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/NJW779/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/NJW779/feedback/</feedback_url>
</event>
<event guid="ced0a845-9d9f-5661-9760-24fdbd3b00ff" id="3399">
<room>CA01</room>
<title>Vector tiles and GeoServer: dynamic vector tiles server, XYZ services, and base maps</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Mapbox vector tiles have emerged as a popular format for delivering geospatial data, offering dynamic rendering and interactivity for modern web maps. While not an official OGC standard, this open specification has been widely adopted, making it a staple of web cartography. This presentation delves into GeoServer's evolving capabilities to serve Mapbox vector tiles, emphasizing recent enhancements and best practices.
We will explore how GeoServer leverages SLD and CSS to define the contents of vector tiles, ensuring tailored and efficient data delivery. New configuration options, such as label point generation, attribute selection and geometry coalescing, will be highlighted as tools to control and optimize tile outputs. Practical advice will also be provided for streamlining vector tile generation, helping users create seamless and scalable workflows.
The session will conclude with a look at how vector tiles can serve as an input for generating high-quality base maps in various coordinate reference systems. Using OpenMapTiles styles and Planetiler, we will demonstrate how to produce visually appealing, multi-projection base maps, unlocking the full potential of vector tiles for diverse applications.
Whether you're building interactive maps or generating custom base maps, this talk will equip you with the knowledge and tools to make the most of GeoServer's vector tile capabilities.</abstract>
<slug>foss4g-europe-2025-3399-vector-tiles-and-geoserver-dynamic-vector-tiles-server-xyz-services-and-base-maps</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/WGZNTZ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/WGZNTZ/feedback/</feedback_url>
</event>
<event guid="c8bf742e-1187-59e0-9391-9bc969efdb96" id="3446">
<room>CA01</room>
<title>State of GeoServer Cloud</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>GeoServer is a well-established and highly popular all-round map server, but reaches some limitations in scalability to meet high loads and high availability requirements. For this purpose, GeoServer Cloud was implemented as a separate open source project built "on top" of GeoServer.
GeoServer Cloud transforms GeoServer into scalable individual components (microservices) for container orchestration environments like Kubernetes.
This talk will give an introduction of GeoServer Cloud and showcase some of the successful usages in production environments.</abstract>
<slug>foss4g-europe-2025-3446-state-of-geoserver-cloud</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/TTWS37/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/TTWS37/feedback/</feedback_url>
</event>
<event guid="47a6f05e-15f9-5119-afe3-b0995f008165" id="3357">
<room>CA01</room>
<title>GeoserverCloud made easy - Start simply and be ready for scaling without pain</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>GeoServerCloud might seem complex, but it doesn’t have to be. In this presentation, we’ll show you how to get started easily and without headaches, leveraging its flexibility and scalability without needing to be a DevOps expert. Through a practical approach, we’ll begin with a local setup using docker-compose, gradually scale up to a small two-machine cluster with shared resources, and finally transition smoothly to Kubernetes. Discover how to grow at your own pace and be ready to scale when your project demands it.</abstract>
<slug>foss4g-europe-2025-3357-geoservercloud-made-easy-start-simply-and-be-ready-for-scaling-without-pain</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/M9WARN/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/M9WARN/feedback/</feedback_url>
</event>
<event guid="a8c147cf-b655-5b07-95be-094771f895eb" id="3394">
<room>CA01</room>
<title>Publishing INSPIRE and other rich data models in GeoServer made easy with Smart Data Loader and Features Templating</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>This presentation will cover the support GeoServer provides to publish rich data models (complex features with nested properties and multiple-cardinality relationships), through OGC services and OGC API - Features, focusing on the recent Smart Data Loader and Features Templating extensions, covering in detail ongoing and planned work on GeoServer.
As far as the INSPIRE scenario is concerned, GeoServer has extensive support for implementing view and download services thanks to its core capabilities but also to a number of free and open-source extensions; undoubtedly the most well-known (and dreaded) extension is App-Schema, which can be used to publish complex data models and implement sophisticated download services for vector data.
We will also provide an overview of how those extensions are serving as a foundation for new approaches to publishing rich data models: publishing them directly from MongoDB, embracing the NoSQL nature of it, and supporting new output formats like JSON-LD which allows us to embed well-known semantics in our data.
Real-world use cases from the organizations that have selected GeoServer and GeoSolutions to support their use cases will be introduced to provide the attendees with references and lessons learned that could put them on the right path when adopting GeoServer.</abstract>
<slug>foss4g-europe-2025-3394-publishing-inspire-and-other-rich-data-models-in-geoserver-made-easy-with-smart-data-loader-and-features-templating</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8YSSQZ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8YSSQZ/feedback/</feedback_url>
</event>
<event guid="e4b72454-88f7-556b-9a38-86e46ea9acd5" id="3498">
<room>CA01</room>
<title>QGIS Web Client goes 3D</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>With QGIS Web Client (QWC) you can publish your maps on the Internet with the same rendering as QGIS Desktop thanks to QGIS Server. The environment consists of a modern responsive frontend written in JavaScript based on ReactJS and OpenLayers. It also offers a 3D view based on Three.js. With qwc-services, an ecosystem of server-side Python/Flask microservices, you can extend the range of functions, for example to control user permissions and edit spatial data in the web application.
QWC is modular and expandable and offers both a standard web application and a development framework. You can start simply and easily with the stock application and then customize your application according to your needs and development capabilities.
This talk will give a short introduction into the QWC architecture and highlight recently added features, especially its 3D capabilities.</abstract>
<slug>foss4g-europe-2025-3498-qgis-web-client-goes-3d</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VLFSLU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VLFSLU/feedback/</feedback_url>
</event>
<event guid="bf00e10d-dec9-5f78-a600-1d73023758d4" id="3515">
<room>CA01</room>
<title>geonetwork-ui to sublime your opendata platform</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>The GeoNetwork-UI project was initially conceived to revitalize the user experience within data platforms, with a particular focus on enhancing data discoverability. The project introduced a compelling vision: leveraging GeoNetwork as an open data platform, seamlessly integrating geospatial data and open data catalogs into a unified, modern, and user-friendly interface.
Over time, the project has not only gained traction but has also evolved significantly. Today, it allows for effortless deployment and customization of datahubs or lightweight metadata editors on top of existing GeoNetwork catalogs. This capability has made GeoNetwork-UI an indispensable tool for organizations seeking to optimize their data management and accessibility.
The presentation will highlight on
- Project Overview: Gain insights into the origins, goals, and evolution of the GeoNetwork-UI project.
- Success Stories: Explore the diverse and innovative applications that have been developed using GeoNetwork-UI, showcasing its versatility and impact.
- Practical Demonstrations: Witness live demonstrations of GeoNetwork-UI in action, highlighting its ease of use and powerful features.
Join us to discover how GeoNetwork-UI is transforming the way we interact with and utilize geospatial and open data. Whether you're looking to enhance your data platform or build new applications, this session will provide valuable insights and inspiration.</abstract>
<slug>foss4g-europe-2025-3515-geonetwork-ui-to-sublime-your-opendata-platform</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/RSYJTJ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/RSYJTJ/feedback/</feedback_url>
</event>
<event guid="d6bf18a4-ac5e-5ee1-a5ac-7c8512f39a6b" id="3395">
<room>CA01</room>
<title>Mastering Security with GeoServer, GeoFence, and OpenID</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>The presentation will provide a comprehensive introduction to GeoServer's own authentication and authorization subsystems. The authentication part will cover the various supported authentication protocols (e.g. basic/digest authentication) and identity providers (such as local config files, databases, LDAP servers, OAuth2/OpenID), covering also cases in which the same source may play both roles (OAuth2, OpenId connect).
It will explain how to combine various authentication mechanisms in a single comprehensive authentication tool, as well as provide examples of custom authentication plugins for GeoServer, integrating it in a home-grown security architecture. We’ll then move on to authorization, describing the GeoServer pluggable authorization mechanism, and comparing it with an external proxy-based solution. We will explain the default service and data security system, reviewing its benefits and limitations.
Finally, we’ll explore the advanced authorization provider, GeoFence. The different levels of integration with GeoServer will be presented, from the simple and seamless direct integration to the more sophisticated external setup. Finally, we’ll explore GeoFence’s powerful authorization rules using:
- The current user and its roles.
- The OGC services, workspace, layer, and layer group.
- CQL read and write filters.
- Attribute selection.
- Cropping raster and vector data to areas of interest.</abstract>
<slug>foss4g-europe-2025-3395-mastering-security-with-geoserver-geofence-and-openid</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8XW8ZQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8XW8ZQ/feedback/</feedback_url>
</event>
</room>
<room name="PA01" guid="16fc454c-4a03-597f-99ce-1866d0ded964">
<event guid="151207c1-3f45-520e-8778-421a5dc834c3" id="3976">
<room>PA01</room>
<title>Comparing 545 Million Years of Sea-Level Change: New insights from the TopoChronia QGIS Plugin</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>Palaeogeography is the study of past geography, focusing on the physical landscapes, climate, and environments of the Earth in past over geological periods. It reconstructs the positions of continents, oceans, mountain ranges, and ecosystems over millions of years, helping scientists understand plate tectonics, past climates, and the evolution of life.
Palaeogeographic maps can be generated with qualitative to semi-quantitative methods, for instance by discriminating between oceans, coastal and land areas, or can be fully quantified, with each pixel of the map being assigned a specific elevation value. Unlike other plate tectonic models, PANALESIS is able to depict fully quantified palaeogeographic maps at 0.1° x 0.1° resolution from 545 million years ago until present-day, in ca.10-million-year time steps.
Palaeogeography can also be leveraged to estimate sea-level variations. By calculating the oceans volumes, and comparing them to the present-day volume, we can quantify the increase or decrease in sea-level required to match this reference. These sea-level variations can then be compared and validated against estimates from other plate tectonic models, and with other methods such as stratigraphic studies.
An initial sea-level curved based on PANALESIS was published in 2015 (Vérard et al., 2015). The methodology was never published in detail and was running on ArcGIS, using a now obsolete system that cannot be run anymore. This implies that it is not possible to reproduce or verify these results.
To address this, we have entirely rewritten and enhanced the source code into a QGIS plugin named TopoChronia. With this paper, we present the new sea-level curve derived from the new palaeogeographic maps and compare them with other data from the literature, including the 2015 PANALESIS data.
We also highlight critical issues that impacted this transition to open-source and open science in general, including input data and source code management practices, mismatch between published results, input data and code, as well as methodological errors, and the way towards FAIR compliance.
We use a straightforward methodology, which converts the model input lines into points, to each of which is assigned an elevation value based on modelling of the geological (or tectonic) setting they belong to (Vérard, 2017). Settings include for instance collision and subduction zones, active or passive margins and mid-oceanic ridges.
A global raster is then interpolated form these points using the QGIS Triangulated Irregular Network (TIN) method, as it has shown to perform well in these circumstances (Franziskakis et al., in prep). From this global raster, we calculate the volume below the elevation of 0m and compare it with the present-day volume of oceans.
Assuming a constant oceanic volume through time, we can therefore estimate the required increase or decrease in sea-level required to match this volume, using Allen &amp; Allen (Allen &amp; Allen, 2005) equations. These equations divide the newly added water column height into an increase of water above initial sea-level (∆SL) and the subsidence (S) of oceanic floor caused by the added water.
We compare the PANALESIS v0 results (spanning form 545Ma to present-day) and we also include the PANALESIS v1 results, currently spanning form 888Ma to 330Ma.
Overall, both the original and the new v0 seem to follow similar tendencies, but with differences in amplitude. The original PANALESIS curve shows lower values compared to the new one, with a median value of +45m. This can be explained by a few factors, including:
1. The reference volume used in 2025 is based on the ETOPO volume under z = 0m, whereas the 2015 reference volume was the 000 Ma (present-day) PANALESIS reconstruction volume, which was significantly higher than ETOPO.
2. The method to calculate the required sea-level rise has changed. For the original version, a 0.55 ratio of the added water column height was used, whereas now the rise is following Allen &amp; Allen equations, which approximates a higher ratio of 0.69. This leads to a 25% higher final sea-level increase.
3. The input data has since changed. Modifications have been made to some features (e.g. assigning a younger age to a feature), leading to large areas being shallower than previously, as depth is primarily controlled by age.
4. A different interpolation method was used, previously Natural Neighbour from ArcGIS, and now replaced by QGIS TIN.
The v1 curve also differs from the v0 ones as the newest version of the model has been strongly enhanced and contains much more details. However, the v1 model only spans from 888 to 330 Ma, allowing comparison only between 330 and 545 Ma.
Improvements are still required on the palaeogegraphy, including the incorporation of climate feedback: simulations for CO2 concentration and precipitation estimates at global scale will help shape better sediment fluxes. It is also important to consider ice sheets formation and melting, strongly controlled by the presence or absence of land in polar regions.
Another aspect is the quantification of error propagation: starting with the input model (time + space), points distribution (space), interpolation (oceans volume, sea-level), orbital parameters related to glacial/interglacial cycles (oceans volume, sea-level).
Finally, the transition to open-source and open data is necessary and underway to make input and output data available, alongside the processing software. This has already started by making the TopoChronia code available online and will contribute to more transparency and reproducibility.</abstract>
<slug>foss4g-europe-2025-3976-comparing-545-million-years-of-sea-level-change-new-insights-from-the-topochronia-qgis-plugin</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/7WXHLS/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/7WXHLS/feedback/</feedback_url>
</event>
<event guid="919e6b7f-8006-5ffa-a058-1767a194dd79" id="4001">
<room>PA01</room>
<title>Integration of HD Maps and Point Clouds: An Efficient 3D Reconstruction Framework for Autonomous Driving Applications</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>Autonomous driving approaches require simulation environments that accurately converge real-world conditions. We can utilize 3D reconstruction methodologies to achieve this. In this paper, we propose a lightweight 3D reconstruction methodology by using the Geospatial Data Abstraction Library (GDAL) and existing HD maps in the OpenDRIVE data format. By leveraging these datasets, we aim to improve the efficiency and accessibility of 3D scene reconstruction for autonomous driving applications. Additionally, we aim to provide a low-cost solution to address the annotation bottleneck in point-wise labeling for the computer vision domain with the constructed 3D models.Scene understanding is crucial for autonomous driving, and most approaches rely on online sensor measurements to extract meaningful information from self-driving cars' surroundings. Visual sensors like cameras and Light Detection and Ranging (LiDAR) are used a lot in autonomous driving for perception tasks. These observations, along with measurements from Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS), can help us understand scenes better. However, the community realized that environmental factors such as weather conditions and illumination can easily affect those measurements. Then high-precision geospatial data became a lifesaver for automated driving. Last decade, the automotive domain has had tremendous interest in high-definition (HD) maps. HD maps are essential and complementary to achieving accurate navigation and localization for autonomous driving. The performance of current perception sensors is limited by their surroundings. This can make it hard for self-driving cars to figure out where they are, especially when they are near the edges of their surroundings, which can make the experience of driving unsafe. Problematic situations include GNSS receivers being affected by multipath effects, LiDAR systems not being able to scan beyond a certain range, and camera image processing algorithms not being able to get clear images that can be used. On the other hand, visual sensors (camera and LiDAR) need pre-annotated and trained datasets to predict and detect objects that are visible in the scene. In contrast to those conditions, HD maps can provide static lane-level information, which allows for an informative baseline about the surroundings in any case.The most common HD map formats include Navigation Data Format (NDS), OpenDRIVE, and Lanelet2. However, differences in road geometry definitions and data structures make cross-format compatibility difficult. Each format has distinct ways of storing and structuring road elements, creating obstacles for seamless data integration across platforms. OpenDRIVE, an industry-standard format developed by the Association for Standardization of Automation and Measuring Systems (ASAM), provides a structured way to describe road networks in lane-level detail.In this work, we will investigate OpenDRIVE's ability to create 3D shapes and explore realistic convergence strategies for autonomous driving simulations. Using the GDAL XODR driver—available since 2024—we will generate 3D geometries as OGC Simple Features for selected road areas. This driver allows the creation of Triangular Irregular Networks (TIN) from driving surfaces and 3D road infrastructure elements, which we will use to generate synthetic point clouds. By leveraging these synthetic point clouds, we can systematically evaluate how well vector-based models approximate real-world environments. This enables a direct comparison between vector-based 3D modeling and real-world LiDAR data/point clouds.Point clouds have been widely used for scene understanding in autonomous driving, as they provide 3D coordinates and intensity values for the environment. However, large-scale 3D modeling is computationally expensive and requires efficient data processing techniques. Annotating these datasets manually is also time-consuming and labor-intensive, making semantic information extraction difficult. The lack of automation in labeling further exacerbates these challenges, slowing down the development of advanced perception models. Addressing these limitations is essential to improving HD map applications and integrating them into broader geospatial workflows.We will use the Iterative Closest Point (ICP) algorithm to make sure that synthetic and real-world data are more closely aligned. This will reduce errors and allow for accurate shape reconstruction. The ability to refine and align synthetic models with real-world measurements is crucial for ensuring high-fidelity simulations. Additionally, we will use Nearest Neighbor Search and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to identify corresponding points and fill missing HD map elements. By intelligently reconstructing missing data, we can enhance the completeness of HD maps and make them more reliable for self-driving applications. This will enhance data completeness and improve overall map reliability for self-driving applications.By integrating these methodologies, we aim to bridge the gap between vector-based HD maps and real-world point cloud data. This will enable a more seamless fusion of geospatial information across different domains, improving both data usability and accuracy. Our method aims to make a 3D reconstruction pipeline that is more accurate and faster. This will make it easier to simulate self-driving cars and help validate and improve HD maps. Ultimately, by enhancing the accuracy and efficiency of 3D modeling techniques, our approach contributes to safer and more effective autonomous driving systems.</abstract>
<slug>foss4g-europe-2025-4001-integration-of-hd-maps-and-point-clouds-an-efficient-3d-reconstruction-framework-for-autonomous-driving-applications</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/3EPQLE/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/3EPQLE/feedback/</feedback_url>
</event>
<event guid="f1b3193c-26ef-5b88-9f92-9b841107e9ed" id="3980">
<room>PA01</room>
<title>Towards Standardization of the EO Data Product Supply Chain – Are OCI Artifacts the Key to Ubiquitous and Scalable EO Data Handling?</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>The Open Container Initiative (OCI) 1.1 specification has expanded container registries beyond traditional software images, enabling them to store and distribute a wide variety of digital artifacts, from software build artifacts to machine learning (ML) models and arbitrarily large data blobs. As the volume of Earth Observation (EO) data generated by satellites and remote sensing applications continues to increase, scalable and efficient distribution methods are becoming essential. OCI registries are well-suited for end-to-end supply chains due to their built-in capabilities for integrity verification and attestations, such as quality assurance, allowing for the application of common tooling and best practices across various steps in the supply chain. Their layered design allows for selective retrieval of specific parts, and optimizations like compression and deduplication can be applied individually, making them ideal for managing EO data of arbitrary size.
## Challenges in Optimizing OCI Registries for EO Data
However, despite these advantages, significant challenges remain in optimizing both client-side parallelization and addressing server-side limitations of existing OCI registries. A critical research question arises: How should EO data be structured within OCI registries to maximize performance? While OCI registries support multiple storage layers and optimizations, the practical implications of storing EO data in this format have not been thoroughly explored. Key concerns include whether OCI registries can effectively support arbitrarily sized EO data and how different storage layouts affect retrieval speed and storage efficiency.
## Investigating Best Practices for EO Data Storage in OCI Registries
This research paper investigates how to structure EO data within OCI registries to optimize performance. By examining various physical data layouts—such as chunking data into blocks or organizing data into multiple layers—the goal is to identify best practices for storing and accessing large EO datasets. Benchmarking common OCI client tooling against a variety of OCI-compliant registries, including public offerings like DockerHub and Quay.io, managed services like AWS ECR, and bespoke cloud-based implementations, will help evaluate retrieval latency, throughput, and parallelization techniques to enhance the efficiency of EO data distribution at scale. The research paper will also examine the impact of different compression, deduplication, and data layout strategies on storage efficiency and retrieval performance.
## Advantages of OCI Registries for EO Data Storage and Distribution
An OCI image, as specified through the Linux Foundation's Open Container Interface, is actually a collection of multiple components. At the top level is an index of all the other included components. It references, in a JSON format, all the other layers with their digest or the cryptographic hash of the content itself. The OCI distribution spec describes how clients pull images from a registry, which is done layer by layer.
OCI registries offer several inherent benefits that make them attractive for EO data storage and distribution. These include:
- Layered Storage Model: OCI artifacts utilize a layered approach, allowing incremental and block-wise storage and retrieval, enabling efficient updates and minimizing redundant data transfers.
- Efficient Distribution: Content-addressable storage allows fetching only changed layers, which minimizes bandwidth and storage costs and supports incremental updates.
- Versioning and Tagging: Version control is inherent in OCI registries, enabling precise tracking of updates. This is crucial as data moves through various stages of processing, validation, and final distribution.
- Attestation and Integrity: Data integrity is ensured using cryptographic hashes, verifying the authenticity and trustworthiness of the supply chain, from raw input to final products.
## Addressing Practical Limitations in OCI Registries
Despite these benefits, the practical limitations of OCI registries for handling large-scale EO data are not fully understood. Specifically, the impact of physical data layout on retrieval speed and storage efficiency requires further investigation. This research paper will explore several strategies for structuring EO data within OCI registries:
- Chunking and Layering Strategies: Investigating whether data should be stored in large monolithic layers or smaller, granular chunks, and evaluating the effects of compression and deduplication on retrieval performance.
- Client-Side Parallelization: Analyzing the impact of parallelized downloads on pull speeds and comparing performance improvements with different concurrent retrieval configurations.
- Server-Side Constraints: Assessing registry performance limits, including bandwidth throttling and API rate limits, and comparing different OCI registry offerings and implementations.
## Benchmarking and Evaluation Metrics
The research paper will employ a benchmark-based approach to evaluate different storage layouts and retrieval optimizations. Key metrics for evaluation include:
- Latency: Measuring the time required to pull (and extract) EO datasets from OCI registries.
- Throughput: Assessing how registry performance scales with concurrent downloads.
- Storage Overhead: Analyzing the efficiency of deduplication and compression techniques.
Test datasets will include EO imagery and EO time-series data stored in cloud-native formats like COGs and Zarrs, which inherently support chunked data structures (compressed and uncompressed). By comparing different layouts and access patterns, insights will be derived into the most effective way to structure EO data within OCI registries.
## Research Questions and Expected Contributions
This research paper seeks to establish best practices for storing EO data in OCI registries by answering the following questions:
- What are the practical limitations of OCI registries for handling arbitrarily large EO datasets?
- How should EO data be physically structured within OCI to optimize performance?
- What are the trade-offs between different storage layouts in terms of retrieval speed, storage efficiency, and scalability?
By systematically evaluating these aspects, this research paper will contribute to the broader adoption of OCI registries for EO data management, ensuring efficient, scalable, and interoperable distribution. The findings will also guide future optimizations in registry implementations to better support large-scale geospatial datasets.
## Goal
OCI registries offer a promising avenue for distributing EO data at scale. However, the performance implications of storing large datasets in this format remain underexplored. This research paper will benchmark various OCI registry implementations, investigating the impact of data structuring, parallelization, and registry limitations. By identifying best practices for EO data storage in OCI registries, the efficiency of geospatial data distribution can be enhanced while leveraging the robust ecosystem of container registries already in place.</abstract>
<slug>foss4g-europe-2025-3980-towards-standardization-of-the-eo-data-product-supply-chain-are-oci-artifacts-the-key-to-ubiquitous-and-scalable-eo-data-handling-</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/HNZK37/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/HNZK37/feedback/</feedback_url>
</event>
<event guid="f1d42110-c455-53fe-b995-d5776b9b141c" id="3994">
<room>PA01</room>
<title>Extracting realistic pedestrian, cycling, and traffic street networks from OpenStreetMap</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>In a time where geospatial information is key to provide context to the world we live in, accurate, realistic, and complete maps are a first-hand necessity. Crowdsourced maps provide a fast, reliable, and affordable way to obtain global geospatial information. Points of interest (POI), street networks, landscape data, and even 3D models are among the information that can be extracted from crowdsourced platforms. In the geospatial community, a project that has dominated the crowdsourced map scene for its reasonable accuracy, and extense community, is OpenStreetMap (OSM).
As OSM has historically been car-centric, deriving realistic and good-quality pedestrian and cycling networks poses a complex task. Various initiatives to improve the quality and amount of pedestrian and cycling data has appeared over the years, specially for big urban centres in high-income countries. Yet, inconsistencies are still present, as the quality of the data in crowdsourced projects varies from one place to another. Those inconsistencies create a dissociation of the street network with the reality.
Building pedestrian networks is increasingly difficult, as walking offers such freedom of movement. While normally in high-income societies there are multiple rules and regulations for pedestrians, low and middle income countries do not follow such rigurosity. Thus, streets that are normally not considered “walkable” in certain places, are realistically used for pedestrian movement, even without the required infrastructure. Another limitation is the level of detail, as big cities normally have very high map detail –including features such as separately-mapped sidewalks and crossroads–, while some other cities only have basic, car-centric, traffic networks that do not offer information about pedestrian capabilities. The situation is less drastic for cycling networks, as normally cycling can be performed over the traffic network. However, more accurate maps would allow the creation of safer and better maps for cyclists.
In this work, we propose and test a methodology for producing realistic pedestrian, cycling, and traffic street networks extracted from OpenStreetMap. The methodology is composed of a generalised set of filters and post-processing methods to produce realistic and usable pedestrian, cycling, and traffic street networks from anywhere in the world. By realistic, we mean that the networks should be as close to reality as possible, while usability is related to the fact that they should allow functional aspects such as routing. To extract the raw networks we used the OSMnx library. Filtering and post-processing is then applied to each raw network for further refinement.
For pedestrian networks, a filter was designed to retrieve all traffic, pedestrian, and cycling street segments that are potentially pedestrian. As a generalisation, each street is considered pedestrian at first, and then, based on certain elimination criteria, non-walkable street segments are eliminated. Elimination criteria includes certain types of streets (e.g., motorways), streets and paths that are non-accessible, cycleways that do not allow pedestrians, and streets that have separately-mapped sidewalks. Particular attention was paid to streets with separately mapped sidewalks, as they provided an important source of inconsistencies. Separately mapped sidewalks provide granularity when mapping pedestrian networks, as it states a clear separation between the geometry of the main road and the geometry of the sidewalk. However, inconsistencies arise when the street segment does not specify that it has a separately mapped sidewalk. The main issue with this kind of inconsistency is the duplication of street segments, increasing the size and complexity of an already complex network, affecting real distances, pedestrian routes, and the calculation of indices based on the network topology. To overcome this, a novel algorithm was implemented to eliminate streets with separately mapped sidewalks based on spatial and angular proximity, i.e., that both a sidewalk and a street segment are close, and their compass angle is similar. As an example, the processing of the pedestrian street network of Mostar, Bosnia and Herzegovina, resulted in a network of 5.354 edges, instead of the original 50.068 edges without elimination, posing a significant reduction. One special remark is that pedestrian street networks are represented as undirected graphs, meaning that every segment can be traversed in any direction.
For cycling street networks, a filter was designed to exclude all non-bikeable segments, as well as segments that clearly specify that cycling is not permitted. Cycling poses less challenges than pedestrian street networks, as regulations for bicycles are normally more strict, and bicycles normally can use the traffic street network. Thus, the cycling network is built on the assumption that bicycles can circulate on any street of the traffic network, and elimination is made based on attributes. Additionally, as cycle networks are similar to traffic, direction is important. This means that cycling networks are represented as directed graphs.
For completeness, the methodology for extracting traffic networks is provided. As OSM is already car-centric, building realistic and usable traffic networks is not a complex task. Nonetheless, special care must be taken towards street direction, as the direction in which traffic can flow is important for traffic. Ergo, traffic street networks are represented also as directed graphs.
An additional advantage of this methodology is that it can be used to spot inconsistencies on the various street networks of OSM, aiding in collaborative mapping efforts. The paper will provide examples on how this methodology can be used to identify duplicated streets and sidewalks, and disconnected street segments.
To conclude, street networks provide valuable information about human mobility and urban dynamics. Its analysis is fundamental for understanding cities and settlements. Having realistic, usable, and open-sourced street network models is then a necessity to analyse, plan, and implement measures for sustainable and resilient cities. This work proposes a novel methodology to extract pedestrian, cycling, and traffic street networks that considers not only functionality, but also real world scenarios.</abstract>
<slug>foss4g-europe-2025-3994-extracting-realistic-pedestrian-cycling-and-traffic-street-networks-from-openstreetmap</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DQYFQC/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DQYFQC/feedback/</feedback_url>
</event>
<event guid="7b39805e-26db-5d99-ab1d-93acd92384f5" id="3970">
<room>PA01</room>
<title>Tracking Urban Heat Island Dynamics Using Open Source Tools and Free Data</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:05</duration>
<abstract>Urban Heat Islands (UHI) represent an increasing challenge in cities worldwide, including smaller urban centres such as Varaždin, Croatia. This study analyses UHI dynamics over a Varaždin city during a ten-year period (2014–2024) using exclusively Free and Open-Source Software (FOSS) and publicly available Earth Observation (EO) data. Landsat 8 thermal imagery was used to calculate Land Surface Temperature (LST), and to analyse its relationship with vegetation cover through the Normalized Difference Vegetation Index (NDVI) and the Proportion of Vegetation (PV). Urban expansion was examined using the Normalized Difference Built-up Index (NDBI). All indices were derived from multispectral and thermal bands using QGIS 3.24 and Python 3.12 libraries including GDAL, rasterio, NumPy, and Matplotlib. The results show a measurable increase in surface temperature, with the average LST rising by +4.41 °C, accompanied by a loss of 3,230 pixels in the dense vegetation class (NDVI &gt; 0.4). Simultaneously, NDBI values indicate expansion of built-up areas across the southern and eastern parts of the city. These changes confirm the spatial transformation towards the urbanization and reduced vegetation cover as main cause of local thermal intensification. This study gives a standardized, open-sourced, transparent and reproducible analysis applicable to other medium-sized cities. The study also explores the potential integration of additional EO sources (Sentinel-3, MODIS, VIIRS) and supporting geospatial data (OpenStreetMap) for enhanced spatiotemporal resolution. The findings highlight the value of FOSS tools and open data in supporting evidence-based urban climate planning and advocate for scalable, cost-effective approaches to UHI mitigation through green infrastructure and adaptive design.</abstract>
<slug>foss4g-europe-2025-3970-tracking-urban-heat-island-dynamics-using-open-source-tools-and-free-data</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>true</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VS7EX9/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VS7EX9/feedback/</feedback_url>
</event>
<event guid="98dfca42-4d25-571d-a50b-fc3d494c6d79" id="3991">
<room>PA01</room>
<title>An Open-Source Deep Learning Framework for Scalable Urban Heat Island Detection Using Geospatial Data</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:35:00+02:00</date>
<start>14:35</start>
<duration>00:05</duration>
<abstract>Increased urbanization rates have had a significant effect on changing land surface characteristics, leading to the rise of Urban Heat Islands (UHIs), localized regions where temperatures are considerably higher than in surrounding rural areas. This phenomenon is primarily driven by dense urban structures, reduced vegetation cover, and anthropogenic heat discharge, which collectively contribute to enhancing the absorption and retention of heat in urban areas (Anjos et al., 2025; Qin &amp; Jiang, 2024). As climate change intensifies, UHIs worsen environmental problems, including increased energy consumption, lower air quality, and severe public health concerns like heat stress and cardiovascular disease (Chanpichaigosol &amp; Chaichana, 2025). The rapid expansion of urban areas has elevated UHI mitigation to one of the highest priorities. Yet, existing detection and analysis methods often lack scalability, automation, limiting their ability to produce high-resolution, globally consistent assessments (Fu et al., 2024).</abstract>
<slug>foss4g-europe-2025-3991-an-open-source-deep-learning-framework-for-scalable-urban-heat-island-detection-using-geospatial-data</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/38KDUJ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/38KDUJ/feedback/</feedback_url>
</event>
<event guid="3d4d163e-b78e-5f66-9872-9bf0d8e95fba" id="3999">
<room>PA01</room>
<title>TEMPORAL ANALYSIS OF MULTISPECTRAL SATELLITE DATA FOR THE PURPOSE OF URBANIZATION MONITORING</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:40:00+02:00</date>
<start>14:40</start>
<duration>00:05</duration>
<abstract>Monitoring the urbanization process and early detection of illegal construction should enable meaningful urban planning and the protection of natural and cultural features of the community. Remote sensing methods can greatly assist in these activities today. There is a major problem of illegal construction in the Republic of Croatia. In 2023 alone, according to a report by the State Inspectorate of the Republic of Croatia, a total of 4,774 inspections were carried out on the territory of the Republic of Croatia, and 1,089 inspection procedures were initiated due to violations of construction regulations. Based on them, 791 decisions were made on the removal of illegal buildings (State Inspectorate of the Republic of Croatia, 2024). Illegal construction undermines the quality of life of individuals and communities because it generally involves disregard for urban planning rules, damages the environment, reduces green areas, increases the risk of urban heat islands, and burdens municipal infrastructure.
This research uses the differences in spectral indices calculated using satellite images from different time periods. The assumption is that the use of the obtained differences in spectral indices from different time periods, in integration with open data in the form of digital orthophoto images and known data on illegal construction, could enable greater expediency of services in sanctioning premature and illegal construction or other environmental devastation.
The aim of this analysis is to examine and determine the possibilities of applying remote sensing methods in the early detection of areas under construction or other types of devastation. The assessment of the quality and usability of the results will primarily focus on the thematic accuracy and reliability of the detection of areas under construction through the use of high and medium spatial resolution images for the evaluation of the results. This research uses images from the Sentinel-2 and PlanetScope satellite systems. The idea was to examine the possibility of using a system that provides free imagery and has problematic spatial resolution for monitoring illegal construction, and a better spatial resolution system that is commercial. The impact of the spatial resolution of the high-resolution satellite imagery of the PlanetScope satellite system (spatial resolution 3 m) and the medium-resolution satellite imagery of the Sentinel-2 satellite system (spatial resolutions 10 m and 20 m) on the accuracy of the obtained results was also analyzed.
Ten spectral indices were calculated using images from the Sentinel-2 and PlanetScope satellite systems for two reference dates (2017 and 2021). The indices were selected by researching relevant literature. One of the results of this research is to highlight the indices that gave the best results for the purpose of detecting illegal construction. The city of Solin (near Split), Croatia, with its wider surroundings, was selected for the research area.
On cadastral plots with a known change in development (it is known that an illegal building was built there), the results obtained for the calculated differences in spectral indices were evaluated through visual analysis. Visual analysis means that the spectral index results are displayed over a digital orthophoto image with a spatial resolution of 0.5 m. Although previous research suggests that the BAEI and NDTI spectral indices should be suitable for separating built-up areas, satisfactory results were not obtained in this study. The same thing happened using the difference of the spectral indices NDBI and DBSI. Both spectral indices did not show good results in cases where smaller buildings were built on mixed-use land, so there is no significant difference in the spectral response for the two observed dates. The BRBA and NBAI spectral indices difference images stood out from the others because in some cases, changes in the structure of gravel and similar materials were successfully detected, and according to previous research, such detection represents the biggest challenge. It is worth highlighting the results obtained using the NBI spectral index, because in the figure, the differences in the spectral index of changes in development stand out best in relation to the environment. The best results were achieved with the difference of NDVI spectral indices calculated using the spectral channels of the Sentinel-2 and PlanetScope satellite systems. According to the achieved results, it is evident that for this type of research, the spatial resolution of the used spectral channels (3 m PlanetScope and 10 m Sentinel-2) did not have a major impact on the accuracy of detection.
After the described analysis, the model was verified in another area, as a blind test. The verification model was conducted with five spectral indices (NDVI, NDVI (calculated with PlanetScope channels), NBI, BRBA and NBAI) which gave the best results. The blind test area was conducted in the Podstrana municipality (also near Split), Croatia. One urban and one rural location within the settlement were selected. From the obtained results, it can be concluded that the detection success of the verification model corresponds to the accuracy of the development model for detecting changes in built-up area, where the analysis with spectral indices NDVI, NDVI (calculated with PlanetScope channels) and NBI showed the best results.
The purpose of this method of detecting illegal construction has proven to be very practical in rural areas where the resulting images of differences were compared with official data on existing illegally constructed structures. From the obtained results, it can be concluded that for areas with a Mediterranean climate, the use of the difference method of spectral indices NBI, NDVI and NDVI (calculated by PlanetScope channels) gives very good results that can be of great use for the initial control of illegal construction.</abstract>
<slug>foss4g-europe-2025-3999-temporal-analysis-of-multispectral-satellite-data-for-the-purpose-of-urbanization-monitoring</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>true</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/8MU9GH/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/8MU9GH/feedback/</feedback_url>
</event>
<event guid="5349df35-fa70-571c-ab46-b41ca2ee14a9" id="3992">
<room>PA01</room>
<title>UNS Novi Sad: LiDAR Dataset for point cloud classification in urban areas</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:45:00+02:00</date>
<start>14:45</start>
<duration>00:05</duration>
<abstract>1.1 Introduction
Automatic and reliable 3D point cloud classification is a crucial yet challenging task with applications across various domains, including urban planning, 3D modeling, and the development of smart cities. Airborne LiDAR (Light Detection and Ranging) has emerged as an efficient and effective tool for conducting large-scale 3D surveys of urban areas, offering high spatial resolution and accurate data collection. Over the years, numerous algorithms and methodologies have been proposed for point cloud classification. Despite advancements in machine learning and deep learning, this task remains a significant challenge in the geospatial community.
One of the primary challenges lies in the availability of sufficient labelled data for training classification algorithms. The creation of publicly accessible, large-scale datasets is essential for developing and benchmarking new methods. While several databases have been introduced, such as ISPRS Vaihingen (Niemeyer, et. al., 2014), LASDU (Ye, et. al., 2020) or AHN3 (AHN, 2024), they have some limitations. For instance, while LASDU and AHN3 datasets are valuable resources for point cloud classification, they lack the comprehensive diversity of urban-specific classes, limiting their utility in capturing the complexity of dense urban environments.
The ISPRS benchmark dataset is most commonly used in resources in this field. It provides a point cloud classified into nine classes, along with features such as x, y, z, intensity, return number, and the number of returns. Additionally, the synchronized orthophoto of the same area is available, providing valuable context for classification tasks. However, this dataset also presents some challenges, including its highly unbalanced class distribution and the relatively small number of points available, particularly for training deep learning methods.
In this paper, we introduced an aerial LiDAR point cloud dataset, UNS Novi Sad, designed specifically for the classification of complex urban environments. The dataset comprises over five million points, classified into seven distinct classes, and is focused on the City of Novi Sad, which is known for its unique urban morphology. The city’s layout reflects the architectural and planning styles typical of Southeastern Europe in the post-World War II era, featuring a mix of high-density residential blocks, green spaces, wide boulevards, narrow streets, and diverse building types. These characteristics ensure that the dataset captures a wide range of structural and spatial variations. In addition to providing new data, we evaluate the PointNet and PointNet ++ algorithm for classification of the proposed dataset.
1.2 Study area
The UNS Geo dataset comprises over five million points, classified into seven distinct classes, and is focused on the City of Novi Sad, which is known for its unique urban morphology. The city’s layout reflects the architectural and planning styles typical of Southeastern Europe in the post-World War II era, featuring a mix of high-density residential blocks, green spaces, wide boulevards, narrow streets, and diverse building types. These characteristics ensure that the dataset captures a wide range of structural and spatial variations.
The study area is in the urban area of Novi Sad (Figure 1.), consisting of Liman, located in the southeast part of the city, and the left Danube bank with high residential blocks, spacious green areas, and boulevards. The topography of the study area is flat, with an average elevation of 77 m. The ALS point cloud data were collected using a Riegl LMS-Q680i laser scanner and a digital camera DigiCam H39 onboard a helicopter.
The total number of annotated points is 5.4 million of points. The dataset is divided into two .las files: for training and for testing.
Table 1. Dataset characteristics
Num. of points Point density [pts/m2]
Training 4.8 M 37
Test 0.6 M 35
In the .las file, each point was assigned the following attributes: Position: X, Y, Z coordinates of each point in UTM 34N (EPSG:32634) projection, Intensity, Return number, Number of returns, Classification, Scan Angle Rank, Time, RGB: Each
Regarding the labeling, the automatic, semi-automatic, and manual classification was used. We selected classes with a focus on different applications such as mapping, urban planning, and forestry monitoring. The points are classified into seven different classes: ground, roads, parking, pedestrian lens, buildings, high vegetation, and cars. The training and testing datasets have a similar distribution, except for pedestrian lenses and cars. The high vegetation class points contain the largest number of points. This is expected since the multiple returns are characteristic of this class and it is also a commonly occurring class in this type of city. The car class only reaches 2.64 % of all labeled points, making them one of the most challenging classes to detect. The imbalance of classes should be considered during the training or testing phase.
1.3 Classification
To provide a brief evaluation of the proposed dataset, the supervised classification to label points was performed. The PointNet architecture is a neural network that directly classifies raw point cloud. PointNet++ applies PointNet to local neighbourhoods to capture local features. The evaluation metrics include the recall, precision, and F1 score.</abstract>
<slug>foss4g-europe-2025-3992-uns-novi-sad-lidar-dataset-for-point-cloud-classification-in-urban-areas</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/YG7B8H/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/YG7B8H/feedback/</feedback_url>
</event>
<event guid="7816d734-9f43-5e1c-bbd9-26f4392afa2f" id="3967">
<room>PA01</room>
<title>Comparative analysis of Sentinel 1 InSAR ground motion data dissemination strategies: towards an optimal model for Serbia</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-17T14:50:00+02:00</date>
<start>14:50</start>
<duration>00:05</duration>
<abstract>Ground Motion Services (GMS) based on Interferometric Synthetic Aperture Radar (InSAR) technology play a critical role in monitoring terrain deformation, assessing geohazards, and supporting infrastructure management. Various European countries have developed national GMS platforms that provide access to ground displacement data derived from Sentinel-1 imagery. These services differ significantly in their dissemination strategies, data accessibility, update frequency, interoperability, and integration with broader geospatial infrastructures. In addition to national initiatives, the European Ground Motion Service (EGMS) offers a harmonized regional dataset, creating opportunities for cross-border analysis while also highlighting disparities in how individual countries distribute and manage their InSAR-based deformation data. Despite the growing importance of these services, no standardized approach to optimal data dissemination has been established, leading to fragmented accessibility, varying user engagement strategies, and inconsistent data integration practices across platforms. The lack of a unified approach also hampers the ability to perform large-scale, integrated analyses of ground motion, limiting the potential for comprehensive geohazard assessments and infrastructure resilience planning across Europe. This study conducts a comparative analysis of existing national GMS platforms, including those in Norway, Germany, Denmark, Sweden, the Netherlands, Romania, Greece, and Italy, alongside EGMS. The research focuses on key aspects of data dissemination, including access policies, distribution formats, visualization tools, update frequencies, and interoperability with other geospatial datasets. The study evaluates open-access models versus restricted or tiered access approaches, examining how different dissemination policies impact the usability of GMS data for scientific, governmental, and commercial applications. Furthermore, it investigates the role of web-based GIS platforms, APIs, and data download services, assessing how these tools contribute to enhancing user experience and providing efficient access to complex geospatial data. Best practices in user interface design and visualization techniques are also explored to ensure that InSAR-derived deformation information is accessible to a wide range of stakeholders, from scientific researchers to government agencies and the general public. The integration of InSAR data with complementary geospatial datasets, such as GNSS measurements and geological surveys, is another key aspect of the analysis. Certain national services incorporate GNSS calibration to enhance data accuracy, while others provide seamless interoperability with national spatial data infrastructures (NSDI) to facilitate broader geoscientific applications. However, inconsistencies in data formats and processing methodologies present challenges to cross-platform compatibility. The study identifies cases where harmonization efforts, such as those promoted by EGMS, improve standardization, as well as instances where national approaches diverge significantly. These inconsistencies often lead to difficulties in comparing and integrating data from different sources, thereby limiting the potential for comprehensive geohazard assessments and early warning systems. Another critical consideration is update frequency and data timeliness. Some GMS platforms, such as those in Italy and Greece, offer high temporal resolution with updates as frequently as every 12 days, whereas others provide annual or irregular updates, limiting their effectiveness for near-real-time monitoring. The balance between data processing efficiency, computational resource demands, and the practical needs of end-users is explored to determine an optimal refresh cycle for ground motion data. Furthermore, the study examines the role of cloud-based processing infrastructures and high-performance computing in enabling large-scale InSAR data management, as demonstrated by Norway’s and Sweden’s platforms. These technological advancements allow for faster processing times, more frequent data updates, and the capacity to handle increasingly large volumes of InSAR data, which is crucial for timely decision-making and hazard mitigation efforts. Despite the significant advancements in InSAR data dissemination, several challenges remain. Variability in data access policies leads to disparities in user engagement, with some services offering unrestricted open data while others require authentication or institutional agreements. The lack of API integration in certain platforms restricts automated data retrieval, limiting interoperability with external applications. Differences in visualization approaches, ranging from interactive web-based viewers to raw data download options, also impact how effectively users can interpret and apply ground motion data. These inconsistencies highlight the need for a more structured framework to ensure accessibility, usability, and scientific robustness in GMS dissemination strategies. Addressing these challenges will help create more effective and inclusive systems for ground motion monitoring that can better serve the needs of diverse stakeholders. Building upon this comparative analysis, the study proposes an optimal dissemination model for a future Serbian Ground Motion Service. The model prioritizes open-access policies, ensuring that ground deformation data is freely available to researchers, decision-makers, and the public. It incorporates a user-friendly web-based GIS interface with interactive visualization tools, API support for seamless integration with NSDI and scientific workflows, and periodic updates that balance computational efficiency with real-time monitoring capabilities. The model also emphasizes interoperability with EGMS to align with European data-sharing standards, allowing Serbia to contribute to and benefit from broader continental ground motion assessments. By adopting such a model, Serbia can enhance its national monitoring capabilities while also fostering collaboration with neighboring countries, creating a more resilient regional infrastructure system. The findings of this research contribute to the ongoing discussion on best practices for ground motion data dissemination, offering a structured approach that can enhance the usability and impact of InSAR-based deformation monitoring. By synthesizing insights from existing GMS implementations, this study provides a foundation for developing a Serbian GMS that maximizes accessibility, ensures scientific rigor, and supports a wide range of applications, from infrastructure resilience to natural hazard assessment. Additionally, the proposed dissemination model will help Serbia become an active participant in international data-sharing networks, enhancing its ability to respond to environmental challenges, improve urban planning, and contribute to regional and global geospatial initiatives.</abstract>
<slug>foss4g-europe-2025-3967-comparative-analysis-of-sentinel-1-insar-ground-motion-data-dissemination-strategies-towards-an-optimal-model-for-serbia</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/E8PQC7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/E8PQC7/feedback/</feedback_url>
</event>
<event guid="7c5afae2-b092-54c2-ae04-b7f192f14538" id="3990">
<room>PA01</room>
<title>Assessing long-term hydrological dynamics and water quality using Google Earth Engine: A case study of Ilgın Lake (1985-2024)</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Monitoring inland water areas is crucial for ecosystem health and water resources management, particularly under impacts of global climate change. However, traditional water management plans prioritize large water bodies due to the labor-intensive nature of data collection and analysis. Consequently, shallow lakes are often overlooked despite their critical role in local ecosystems. This situation is critical, as shallow lakes like Ilgın Lake have unique importance for migratory bird populations and irrigation-dependent agricultural livelihoods. Recent advancements in cloud-based platforms like Google Earth Engine (GEE) enable efficient, scalable remote sensing analyses and democratize access to a wide range of data sources. This study leverages the GEE Python API and free and open-source Python libraries (e.g., geemap, scipy, pymannkendall, pingouin) to present a scalable workflow for assessing hydrological and water quality dynamics in shallow lakes. The methodology is demonstrated through a 40-year (1985-2024) case study of Ilgın Lake in Central Anatolia, Türkiye. Ilgın Lake is a vital resource for regional agriculture; however, its shallow nature increases vulnerability to climate change and human activities, necessitating continuous monitoring. This lake is also classified as a protected area and a nitrate vulnerable zone under the European Union Water Framework Directive (WFD). Despite this designation, to the best of our knowledge, there is no specific conservation action plan or regular in-situ water quality monitoring program.
We conducted a long-term analysis (1985-2024) of water area changes and water quality parameters to investigate their relationship with key climate factors. Annual water areas were derived using the Modified Normalized Difference Water Index (MNDWI) applied to Landsat 5/7/8 satellite images, with dynamic Otsu thresholding (Otsu, 1979; Xu, 2006). The Otsu method is reliable, especially for shallow lakes, as it automatically selects the best threshold by maximizing inter-class variance between water and non-water pixels. A total of 347 Landsat scenes were processed using the GEE Python API, incorporating cloud masking and gap-filling for Landsat 7 scan-line corrector off data. The accuracy of water area extraction was validated using high-resolution Google Earth image with random sampling points. Based on 250 sample points, a binary confusion matrix was constructed, and overall accuracy (96.0%) and kappa coefficient (0.887) were calculated. Trends were analyzed using non-parametric statistical methods (Mann-Kendall and Theil-Sen), and correlations with key climate variables (total precipitation, mean temperature) were assessed using the ERA5 (ECMWF Reanalysis Fifth Generation) dataset. Water quality within water-masked areas was assessed via the Normalized Difference Chlorophyll Index (NDCI) (Mishra and Mishra, 2012) for chlorophyll and the Normalized Difference Turbidity Index (NDTI) (Lacaux et. al., 2007) for turbidity. Relationships between climate variables, water area, and water quality were evaluated using Pearson correlation and multiple linear regression. Partial correlation analysis was used to isolate the effects of temperature and precipitation. Multiple linear regression was used to quantify the combined influence of temperature and precipitation on water area variations.
The results showed that Ilgın Lake experienced a significant decrease in water area (p &lt; 0.05) at a rate of -9.54 hectares/year. The lake lost 31% of its area between 1985 and 2024. Annual mean temperature showed a significantly increasing trend (p &lt; 0.01) at a rate of 0.05 °C per year. For water quality, chlorophyll concentrations (NDCI) significantly increased (p &lt; 0.01), indicating intensifying eutrophication. These trends are related to agricultural runoffs and warmer temperatures. The temperature was found to be negatively correlated with water area (r= -0.45) and positively correlated with NDCI (r= 0.40). Multiple linear regression revealed that temperature and precipitation explain 21% of the annual water area variability (p &lt; 0.05). Incorporating 1-year precipitation lags improved the explanatory power (R2= 0.34), highlighting delayed hydrological responses in shallow lakes. The remaining unexplained variance (66%) suggests additional anthropogenic drivers, such as agricultural water use and runoff. This aligns with public documentation under Türkiye’s EU WFD commitments, as Ilgın Lake is designated as a nitrate vulnerable zone and protected area.
These findings underscore the vulnerability of shallow lakes like Ilgın Lake to ecological degradation, driven by both climatic variations and human activities. Their limited water depth increases risks to sustainable agriculture, biodiversity, and local socio-economic conditions. The proposed workflow utilizes open datasets on the cloud-based GEE platform and open-source Python tools, ensuring cost-effective scalability. All code and workflow are publicly available as Jupyter Notebook on GitHub (https://github.com/earth-obs/lake-gee-hydrology-water-quality) under the open source MIT license. This approach provides valuable insights into sustainable water resource management plans, especially for regions where field data is unavailable. This study aligns with the EU WFD goals by providing cost-effective and scalable sources for monitoring water bodies listed under Annex V. We conclude that water resource monitoring studies should focus not only on the hydrological context but also on water quality status, as both are essential for holistic water management. Additionally, shallow lakes like Ilgın play a critical role in preserving natural habitats and sustaining local agricultural livelihoods. Future work will extend this framework to higher spatial and spectral resolution satellite imagery (e.g., Sentinel-2) and additional shallow lakes across Europe.</abstract>
<slug>foss4g-europe-2025-3990-assessing-long-term-hydrological-dynamics-and-water-quality-using-google-earth-engine-a-case-study-of-ilgn-lake-1985-2024-</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/JZRLA8/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/JZRLA8/feedback/</feedback_url>
</event>
<event guid="7cd75ddd-2d67-52a6-8a10-a79e7589358e" id="3968">
<room>PA01</room>
<title>Utilizing Sentinel-2 Remote Sensing for Water Quality Monitoring in Deran Lake, Bosnia and Herzegovina</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Climate change significantly threatens water quality, ecosystem health, and the balance of lake ecosystems, making the monitoring of lake water quality increasingly critical. Remote sensing technology has emerged as an effective tool for this purpose. This study focuses on Deran Lake in Bosnia and Herzegovina, assessing the suitability of Sentinel-2 Multispectral Instrument (MSI) data for mapping various water quality parameters. Deran Lake is a vital aquatic habitat known for its rich biodiversity, primarily due to the numerous water sources originating from the surrounding karst hills. Its unique hydrological connection with the Krupa River and various underground springs enhances its ecological significance, providing a habitat for diverse plant and animal species. Recognized for its ecological importance, Deran Lake has been designated as part of the Hutovo Blato Nature Park, a wetland area that forms a natural unit of the Neretva River delta in southern Bosnia and Herzegovina. This nature park encompasses six lakes, including Deran, Jelim, Drijen, Orah, Škrka, and Svitava, and is sustained by 62 underground freshwater springs. Due to the lake’s shallowness, its dimensions vary with seasonal changes, covering approximately 1.4 square kilometres during high water levels and shrinking to about 0.3 square kilometres in summer. The Krupa River serves as the lake's sole outflow, flowing into the Neretva River, and plays a crucial role in maintaining the ecological balance of the area. The Krupa River's hydrological dynamics are complex; under certain conditions, it can reverse its flow during high water levels, returning water to the lake. This phenomenon significantly impacts nutrient dynamics and ecosystem changes, necessitating comprehensive monitoring for thorough understanding of these processes to preserve ecological balance. Deran Lake is characterized by its pristine natural environment, with minimal anthropogenic pressure, although strong seasonal vegetation complicates water quality studies. In summer, extensive water lily growth can cover nearly the entire lake surface, obstructing various research methods. The dense vegetation, particularly water lilies, poses challenges for remote sensing and the assessment of physicochemical and biological parameters. Additionally, this extensive vegetation cover can influence nutrient distribution and other ecological factors, complicating the assessment of ecosystem processes at the lake level. To mitigate these challenges, the research focuses on a high-water level period when vegetation cover is reduced, allowing for more effective remote sensing research to contribute to the understanding and conservation of the Hutovo Blato ecosystem. By using new technologies, researchers can focus their efforts on better understanding the role of vegetation in nutrient distribution and the effects of seasonal changes in the lake's ecosystem by continuous monitoring in long-term studies and research of the ecosystem is essential to develop strategies for its protection. The application of new technologies contributes to more effective monitoring and conservation of these valuable ecosystems. Ultimately, such approaches can contribute significantly to the sustainability of the Hutovo Blato ecosystem and ensure the long-term conservation of biodiversity. With proper management and the application of scientific research, the negative effects of climate change can be mitigated and the balance in these fragile ecosystems can be maintained. This research aims to explore the application of Sentinel-2 imagery for monitoring water quality, specifically focusing on Deran Lake. Sentinel-2, part of the Copernicus Programme, is operated by European Space Agency and provides high-resolution optical imagery from 10 m to 60 m on a free and open data basis. The mission includes the Sentinel-2A and Sentinel-2B satellites, with a third satellite, Sentinel-2C, launched in 2024, and plans for a Sentinel-2D in the future to replace the earlier satellites. The mission supports a variety of applications, including agricultural monitoring, emergency management, land cover classification, and water quality assessment. Sentinel-2 features multi-spectral data with 13 bands that cover visible, near-infrared, and short-wave infrared spectra, allowing for systematic global coverage from 56° S to 84° N and a revisit time of every 5 days. The mission’s spatial resolutions of 10 m, 20 m, and 60 m, along with a 290 km field of view and a free and open data policy, make it a valuable tool for environmental monitoring. The study emphasized the importance of lakes and the growing demand for water quality monitoring at both local and global scales. The research evaluates the effectiveness of Sentinel-2's Multispectral Instrument (MSI) data in mapping various water quality parameters, including chlorophyll-a, total suspended solids, and water transparency. In situ measurements from Deran Lake were compared with remote sensing assessment derived from atmospherically corrected Level-1C images. Since 2015, Sentinel-2 Level 1C products have been available globally, providing Top of Atmosphere (TOA) reflectance images. The study employed the C2RCC processor for atmospheric correction in the Sentinel Application Platform (SNAP), and all bands were resampled to a uniform resolution of 10 m for comparability. SNAP is a versatile architecture designed for Earth observation processing and is available free of charge to the Earth Observation Community. C2RCC, developed by Schiller and Doerffer in 1999, utilities a machine learning-based methodology for atmospheric correction and in-water retrieval challenges. The processor was utilized to assess water parameters, yielding correlation results with R² greater than 0.5 for the parameters examined. These initial findings suggest that Sentinel-2 could be a valuable resource for lake monitoring and research, particularly due to the routine availability of data over the years, frequent imagery, and free and open data policy.</abstract>
<slug>foss4g-europe-2025-3968-utilizing-sentinel-2-remote-sensing-for-water-quality-monitoring-in-deran-lake-bosnia-and-herzegovina</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/EYUWHU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/EYUWHU/feedback/</feedback_url>
</event>
<event guid="8705f800-3175-5462-8dd6-acc58184007d" id="4000">
<room>PA01</room>
<title>Urban Change Detection in Tirana, Albania (2000-2025) Using Remote Sensing and Open Geospatial Data</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-17T16:30:00+02:00</date>
<start>16:30</start>
<duration>00:30</duration>
<abstract>Urban Change Detection in Tirana, Albania (2000-2025) Using Remote Sensing and Open Geospatial Data
Tirana, the capital of Albania, has experienced rapid urbanization over the past two decades, driven by political, social, and economic transformations. This fast and often chaotic growth has led to significant changes in land use, urban expansion, and environmental conditions. Uncontrolled urbanization presents major challenges for sustainable city development, requiring a comprehensive understanding of urban change dynamics. This study analyzes the spatial and temporal dynamics of urban change in Tirana from 2000 to 2025, using open geospatial data and satellite image processing techniques to map and examine transformations in land cover and urban development.
To assess land cover changes, the study utilizes high-resolution satellite imagery from Landsat and Sentinel-2, which provides valuable information on the spatial extent of urbanization and land use transformations over the past two decades. Additionally, open data from Urban Atlas is integrated to enhance classification accuracy and enable a more detailed analysis of land cover changes. Open data from sources such as the State Authority for Geospatial Information in Albania (ASIG) is also used to validate the results, ensuring that findings reflect actual changes on the ground and providing a comparison with existing datasets.
The methodology of this study is based on change detection techniques, which are essential for understanding urban growth. Key indices such as the Normalized Difference Vegetation Index (NDVI) are used to monitor vegetation loss, while the Normalized Difference Built-up Index (NDBI) is applied to track the expansion of built-up areas. These indices help quantify land cover changes by distinguishing between urban areas, vegetation, and other land uses. NDVI is particularly useful for detecting vegetation loss, often associated with urban sprawl and land degradation. Similarly, NDBI serves as an effective indicator for monitoring the increase in built-up areas, a crucial aspect of urban expansion.
The study employs a supervised classification approach to categorize land cover into different classes using the Random Forest algorithm. This machine learning technique has proven effective in classifying land cover with high accuracy, especially in complex landscapes such as urban areas. The Random Forest algorithm combines multiple decision trees to classify pixels in satellite imagery, allowing for the differentiation of urban, vegetation, water, and other land use types. By ensuring a high level of classification accuracy, the study provides a reliable assessment of urban changes in Tirana over time.
Beyond analyzing past urban growth, the study also aims to predict future urban development trends. To achieve this, the MOLUSCE plugin in QGIS is used to model future urban growth patterns based on historical data and influencing factors such as population growth, infrastructure expansion, and policy interventions. The MOLUSCE tool enables the prediction of land use changes and urban expansion over time, helping to outline future development scenarios in Tirana. Population data from the Albanian Institute of Statistics (INSTAT) is integrated into the analysis to better understand the driving factors behind urban change. This data provides insights into population growth trends, a key driver of urbanization, and their interaction with other urban development factors.
The results of this study provide a comprehensive analysis of urban change in Tirana, offering valuable insights into the city's transformation from 2000 to 2025. The study highlights the extent of uncontrolled urbanization, vegetation loss, and the expansion of built-up areas, which are characteristic features of rapid urbanization. These findings are crucial for urban planning and policy development, as they offer a data-driven foundation for understanding the drivers of urban growth and the challenges associated with managing it. The results can aid in designing sustainable urban development strategies, helping policymakers and urban planners better anticipate and manage future growth, mitigate negative environmental impacts, and improve the quality of life for city residents.
Also, methodology and findings of this study have broader applications beyond Tirana. The approach used can be applied to other cities experiencing similar urbanization patterns, providing a valuable tool for urban planners, researchers, and policymakers globally.
By leveraging open geospatial data and advanced satellite image processing techniques, this study not only contributes to the FOSS4G community's efforts to understand urban change but also enhances the overall sustainability and informed management of urbanization. The methodology applied here is a prime example of how open geospatial data can improve urban research, as it facilitates the accessibility, transparency, and reproducibility of urban analyses. Through the use of freely available resources, the study significantly improves land cover change detection, reinforcing the growing body of research emphasizing the importance of open data in addressing global urbanization challenges.
Furthermore, the availability of open data empowers local communities to actively participate in the urban planning process, fostering public awareness and engagement. By making data accessible, the study strengthens social cohesion and builds trust between citizens and authorities, facilitating better-informed decision-making that contributes to the sustainable development of cities.</abstract>
<slug>foss4g-europe-2025-4000-urban-change-detection-in-tirana-albania-2000-2025-using-remote-sensing-and-open-geospatial-data</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FTRJJA/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FTRJJA/feedback/</feedback_url>
</event>
</room>
</day>
<day index="3" date="2025-07-18" start="2025-07-18T04:00:00+02:00" end="2025-07-19T03:59:00+02:00">
<room name="KOS" guid="76dfe3d7-bde5-5712-8dd5-f2b92782aa23">
<event guid="34441800-ec9e-5fb6-9575-af99863611d0" id="3721">
<room>KOS</room>
<title>Closing Ceremony</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T18:00:00+02:00</date>
<start>18:00</start>
<duration>00:30</duration>
<abstract>Concluding remarks on the conference by the Mostar LOC</abstract>
<slug>foss4g-europe-2025-3721-closing-ceremony</slug>
<track>Plenary</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/KHBKXJ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/KHBKXJ/feedback/</feedback_url>
</event>
</room>
<room name="EL11" guid="e2a70c93-299e-589b-b24b-95b372b81974">
<event guid="3282b757-26b8-55e0-9acd-f0c6cf8db9c7" id="3397">
<room>EL11</room>
<title>Certified GeoServer: status of OGC service and format compliance</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>This presentation chronicles the evolution of GeoServer’s compliance with OGC standards, detailing both past challenges and recent achievements. GeoServer has long been committed to open standards, historically running CITE tests to validate its OGC services. However, due a build server vandalism event, daily checks for CITE compliance got lost.
The GeoServer PSC eventually sponsored a new way for running automated tests, which succeeded, but eventually failed to achieve completion of making the test pass again, and to reinstate checks as a daily, operational activity.
A turning point came during the 2024 OGC API Features sprint, which served as the catalyst for reinstating CITE test automation, for OGC API Features itself, along with a couple of other services that did not require further work.
Recognizing the importance of standards compliance for users and the broader geospatial community, the GeoServer team began adding more more services to the effort, performing a combination of fixes in the GeoTools and GeoServer projects, as well as a productive collaboration with OGC to fix some issues in the CITE tests themselves.
By leveraging GitHub Actions, the project now continuously validates compliance across a broad spectrum of supported services—including WMS, WMTS, WFS, WCS, and OGC API Features—and widely used data formats such as GeoTIFF, GeoPackage, and KML. These tests are now a key part of the pull request workflow, ensuring that new changes maintain or improve compliance.
Finally, the presentation outlines the roadmap for achieving re-certification, which includes formal validation with OGC over a well known server, which might also help GeoServer become a new reference implementation as well. By re-establishing certification, GeoServer aims to reinforce its reputation as a reliable, standards-compliant platform for geospatial data and visualization services.</abstract>
<slug>foss4g-europe-2025-3397-certified-geoserver-status-of-ogc-service-and-format-compliance</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/YFFDVB/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/YFFDVB/feedback/</feedback_url>
</event>
<event guid="b96928ff-ec41-54a6-ae1e-8e737903026d" id="3489">
<room>EL11</room>
<title>State of GeoNetwork</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>The GeoNetwork-opensource project is a catalog application facilitating the discovery of resources within any local, regional, national or global "Spatial Data Infrastructure" (SDI). GeoNetwork is an established technology - recognized as an OSGeo Project and a member of the foss4g community for over a decade.
GeoNetwork is the most successful catalog application in Europe helping organizations and public institutions to share their story. We are always active, exploring the next chapter, representing foss4g at EUR in Paris, and OGC in Rome.
The GeoNetwork team would love to share what we have been up to in 2025!
The GeoNetwork team is excited to talk about the different projects that have contributed with the new features added to the software during the last twelve months. Our rich ecosystem of schema plugins continues to improve; with national teams pouring fixes, improvements and new features into the core application.
We will also talk a bit about the GeoNetwork team and our plans. Progress of our main branches (4.2.x and 4.4.x), release schedule, and a peak at the plans for GeoNetwork 5.
Attend this presentation for the latest from the GeoNetwork community and this vibrant technology platform.</abstract>
<slug>foss4g-europe-2025-3489-state-of-geonetwork</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/YNJKWD/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/YNJKWD/feedback/</feedback_url>
</event>
<event guid="759f9e39-bdcd-53b3-ad47-69ee8e31eead" id="3226">
<room>EL11</room>
<title>pygeometa project status</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>pygeometa provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files (affectionately called metadata control files [MCF]). Leveraging the simple but powerful YAML format, pygeometa can generate metadata in numerous standards. Users can also create their own custom metadata formats which can be plugged into pygeometa for custom metadata format output.
For developers, pygeometa provides a Pythonic API that allows developers to tightly couple metadata generation within their systems and integrate nicely into metadata production pipelines.
The project supports various metadata formats out of the box including ISO 19115, the WMO Core Metadata Profile, and the WIGOS Metadata Standard.
pygeometa has minimal dependencies (install is less than 50 kB), and provides a flexible extension mechanism leveraging the Jinja2 templating system.
This presentation will provide an update on recent enhancements, use in high profile projects as well as future plans and roadmap.</abstract>
<slug>foss4g-europe-2025-3226-pygeometa-project-status</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/AA3UWB/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/AA3UWB/feedback/</feedback_url>
</event>
<event guid="556fc398-61d5-5506-8d4e-8de7ed4fbaa6" id="3412">
<room>EL11</room>
<title>State of MapStore</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>MapStore is an open source product developed for creating, saving and sharing in a simple and intuitive way maps, dashboards, charts and geostories directly online in your browser. MapStore is cross-browser and mobile ready, it allows users to:
- Search and load geospatial content served using widely used protocols (WMS, WFS, WMTS, TMS, CSW, 3D Tiles) and formats (GML, Shapefile, GeoJSON, KML/KMZ etc..)
- Manage maps (create, modify, share, delete, search), charts, dashboard and stories directly online
- Manage users, groups and their permissions over the various resources MapStore can manage
- Edit data online via WFS-T with advanced filtering capabilities
- Deeply customize the look&amp;feel to follow strict corporate guidelines
- Manage different application contexts through an advanced wizard to have customized WebGIS MapStore viewers for different use cases (custom plugins set, map and theme)
You can use MapStore as a product to deploy simple geoportals by using the standard functionalities it provides but you can also use MapStore as a framework to develop sophisticated WebGIS portals by reusing and extending its core building blocks.
MapStore is built on top of React and Redux and its core does not explicitly depend on any mapping engine but it can support both OpenLayers, Leaflet and Cesium; additional mapping engines could be also supported to avoid any tight dependency on a single engine.
The presentation will give the audience an extensive overview of the MapStore functionalities for the creation of mapping portals, covering both previous work as well work for the future releases. Eventually, a range of MapStore case studies will be presented to demonstrate what our clients (like City of Genova, City of Florence, Halliburton, Austrocontrol and more) and partners are achieving with it.</abstract>
<slug>foss4g-europe-2025-3412-state-of-mapstore</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/UMP8UC/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/UMP8UC/feedback/</feedback_url>
</event>
<event guid="f0d58681-670e-5428-996e-e586099bfe1a" id="3315">
<room>EL11</room>
<title>Migrate and Synchronize GeoServer configuration with Terraform</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>The Terraform Provider for GeoServer configuration management keeps improving. After an overview of the changes of the past year, we will see how to take advantage of the terraform ecosystem to make configuration management easier. We will finish to see how you can use the provider together with terraformer to synchronize environments or to migrate from GeoServer to GeoServer Cloud.</abstract>
<slug>foss4g-europe-2025-3315-migrate-and-synchronize-geoserver-configuration-with-terraform</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/KRFJWZ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/KRFJWZ/feedback/</feedback_url>
</event>
<event guid="d3871712-bd6d-5ece-a16e-71f68467b41a" id="3388">
<room>EL11</room>
<title>OGC APIs with GeoServer: implementation, availability, and next steps</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>The OGC APIs are a fresh take at doing geo-spatial APIs, based on WEB API concepts and modern formats, including:
- Small core with basic functionality, extra functionality provided by extensions
- OpenAPI/RESTful based
- JSON first, while still allowing to provide data in other formats
- No mandate to publish schemas for data
- Improved support for data tiles (e.g., vector tiles)
- Specialized APIs in addition to general ones (e.g., DAPA vs OGC API - Processes)
- Full blown services, building blocks, and ease of extensibility
This presentation will provide an introduction to various OGC APIs and extensions, such as Features, Styles, Maps and Tiles, STAC and CQL2 filtering. Some of specs are finalized and complete enough that they have a GeoServer supported extensions, while others are provided as community modules. Join us to find out the current state of implementation, our future steps, and how you can participate in it.</abstract>
<slug>foss4g-europe-2025-3388-ogc-apis-with-geoserver-implementation-availability-and-next-steps</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/U7KESQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/U7KESQ/feedback/</feedback_url>
</event>
<event guid="329dfae7-95dd-52b6-aa8b-97fdfcf08b9e" id="3372">
<room>EL11</room>
<title>Overture Maps as a datasource within GeoServer</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Overture Maps provides a rich source of open geospatial data, but how can we harness its full potential within GeoServer without the complexity of local data management? In this presentation, we will explore how Overture Maps can be seamlessly integrated as a direct data source in GeoServer, unlocking new possibilities for dynamic map rendering and real-time geospatial analysis.
Through practical examples, we will demonstrate different approaches to accessing and utilizing Overture Maps data within GeoServer, enabling dynamic rendering without the need for local storage. Whether through a Web Feature Service (WFS) gateway or alternative integration methods, our approach ensures direct access to up-to-date geospatial information. Additionally, we will introduce enhancements developed to streamline the use of Overture Maps as a data source, making it easier to integrate and leverage its full potential.
Join us to discover how this integration simplifies workflows, optimizes performance, and enables real-time geospatial applications powered by open data.</abstract>
<slug>foss4g-europe-2025-3372-overture-maps-as-a-datasource-within-geoserver</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/KSPKZQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/KSPKZQ/feedback/</feedback_url>
</event>
<event guid="49d75d82-6fa7-5dc7-a613-6658eb5dbb03" id="3470">
<room>EL11</room>
<title>Sharing Online Maps and Beyond – Hub4Everybody</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Save vectors and rasters in a cloud. Share them via OGC services. Create map compositions from the data. Create web pages with your map compositions. Modify data and map compositions from QGIS and QField. Connect maps with e-learning courses. Collaborate with other people. All of this can be done with Hub4Everybody. No programming needed to start, everything is done from a web browser. Custom setup of tools is possible with a self-hosted solution. What are we using it for? And for what purpose you might want to use it?
Hub4Everybody is a one-of-a-kind solution for publishing, sharing and cooperative management of geographical datasets, such as professional data and measuring, results of research projects or student papers, educational materials, emotional maps, visualization of in-field research and other maps, tables, or databases. You can easily upload or update your data as well as adjust the parameters of sharing among different audiences. Hub4Everybody is an alternative tool combining online office software with an editorial system for spatial data. It is also an open-source alternative to already existing commercial solutions, while offering additional extending options. Hub4Everybody offers all usual functions of geo-portals (working with a map, linking of external data and services) but on top of that it offers a possibility to link desktop and mobile solutions for geographical data processing, data visualisation in form of storyboard and communication components via social networks. The solution is scalable and fully adaptable to the end-user needs. You can store your data directly on Hub4Everybody cloud or in your own infrastructure. All technologies used for Hub4Everybody are open source, which enables you to communicate with all kinds of users all over the world while no costs are necessary.</abstract>
<slug>foss4g-europe-2025-3470-sharing-online-maps-and-beyond-hub4everybody</slug>
<track>Building a business with FOSS4G</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DFNMRR/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DFNMRR/feedback/</feedback_url>
</event>
</room>
<room name="SA01" guid="b2c2c705-1015-5f82-b5c4-38f381b9eb7c">
<event guid="917c0477-a76c-5f59-96b8-1fac68edafbf" id="3401">
<room>SA01</room>
<title>NTRIP Reference Systems: Is there a solution to the lack of data?</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>NTRIP (Networked Transport of RTCM via Internet Protocol) is a protocol to get the RTK corrections in your GPS device which is frequently used nowadays.
Neither RTCM3 messages (as of version 3.3 of the standard), nor the NTRIP handshake include any clarification about the coordinate reference system (CRS) that applies to the corrected coordinates.
"NTRIP-catalog" is an open-source open-data project to create a database that will allow any application to get a good understanding of the CRS that should be used with each provider.
This talk will explain the initial problem, how it is a nightmare for many users, and how we try to fix it with this project that is open to NTRIP providers and consumers.</abstract>
<slug>foss4g-europe-2025-3401-ntrip-reference-systems-is-there-a-solution-to-the-lack-of-data-</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DG3BF7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DG3BF7/feedback/</feedback_url>
</event>
<event guid="2275e217-ef70-5a2a-aceb-4968fcb7f7b2" id="3418">
<room>SA01</room>
<title>Layperson walk into open geospatial clergy</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>The last year of my university years was a horrible time. You need money; you studied business; you actually wanted to be an actor, why did you study business; you still have some dignity salted with ego so that you don't wanna get melted into a corporate; you think maybe academia is the way; you think who is gonna afford another investment for studying something nobody cares anymore and there are already millions "copy" of you out there; you search for something meaningful in life for yourself although you know very little about yourself; you really need money.
Amidst this chaotic early youth mantra, I find myself in an agri-tech startup in İstanbul where I can at least afford to live. After my first dearest task of researching marijuana legalization, somehow, I ended up researching how to classify satellite imagery to detect tomato fields. An economist guy from my university, Sinan, showed me how he visualizes an NDVI band literally in Excel by adjusting cells and giving them numbers and colors accordingly. Luckily, we got Betül doing her masters in geomatics at that time, and she said: "Look kids..." while we were looking with an empty face and started to explain the business here.
After getting the basics from Betül, I did what I've been doing since I was 7 years old: I started to surf the internet alone. My walk started into this clergy of people sharing everything they know with me. Contacted many people on Linkedin, watched Youtube videos, read blogs, never understood PyQGIS, hired by a guy among those people and moved to Berlin, where I started acting after years, attended FOSS4Gs, saw many in-person that I was stalking online, met randomly with people from Yer Çizenler &amp; HOT during a disaster relief, felt some meaning, felt in my nerves deeply that I'm not alone in this shapeless Earth, at least not always.
I'll try to summarize what I've found that helped me from the 0-state getting into the industry for the kids like me out there.
Ćao, djeco!</abstract>
<slug>foss4g-europe-2025-3418-layperson-walk-into-open-geospatial-clergy</slug>
<track>Open community</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/AY9GE8/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/AY9GE8/feedback/</feedback_url>
</event>
<event guid="f28eee95-cef7-5c4c-a306-7bbfdbd99b47" id="3494">
<room>SA01</room>
<title>QGIS - what the hack is this?</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>This talk will focus on lessons learned teaching the use of QGIS software through various workshops in Slovenia. Workshops must have strait forward learning path with useful results – let’s make a map. The learning process for workshops must follow client organizations requirements and must be tailored to their needs, from basic QGIS skills to using geodatabases with PostGIS and PostgreSQL or to using mobile apps to enhance their own workflow. Workshops are roughly divided into three levels respectively beginner, advanced and professional and enable new users from current level of competence towards a better level of competence. I especially prefer beginners, because they can be taught FOSS4G from the scratch. The users must have an option to ask questions at any time of the course. It is necessary to modify existing courses or add new content to follow the QGIS development and also to follow new open data datasets. Open data are also important part of the teaching and the combination usually intrigue new users to see the added value to their knowledge and work. Teacher must be flexible to answer any question regarding QGIS and open data and the answer ‘I don’t know’ is also good. This means I will give answer later when I will look up the topic. QGIS workshops also give me a chance to give tasks to participants to check their level of knowledge and sometimes give them different perspective on analysis to solve.</abstract>
<slug>foss4g-europe-2025-3494-qgis-what-the-hack-is-this-</slug>
<track>FOSS4G in education and research</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/HTUASB/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/HTUASB/feedback/</feedback_url>
</event>
<event guid="e95ef85b-9adb-53cf-bcd3-dab68e43035b" id="3316">
<room>SA01</room>
<title>GeoStyler - Support for ArcGIS and other new Features</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>This talk shows the latest features of GeoStyler - the converter for map styles - as well as the achievements of the last Community Code Sprints. Here, we will focus on the support for converting from ArcGIS styles to open formats like OpenLayers and QGIS styles, and thereby lowering the barrier of getting out of a vendor lock-in of geospatial software. We will also talk about the potential of GeoStyler as an ad-hoc converter of styles in combination with the new OGC API. Future plans, such as becoming an official OSGeo project, as well as upcoming events will be presented as well.</abstract>
<slug>foss4g-europe-2025-3316-geostyler-support-for-arcgis-and-other-new-features</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/DWGZX3/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/DWGZX3/feedback/</feedback_url>
</event>
<event guid="4ecc9eda-a513-5d59-b485-ad05cb06f170" id="3480">
<room>SA01</room>
<title>PDF Map Generation as Headless Chrome Service</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>#### Summary
In the context of a project for Swisstopo, the Federal Office of Topography of Switzerland, we analyzed the current technological landscape for generating PDF files for print from vector tile-based maps in online map services. One of our main goals was to identify potential technology stacks supporting full backend rendering, full frontend rendering, or hybrid approaches. While testing various options, we developed a universal print backend based on Headless Chrome, Puppeteer and Node, which corresponds to a full backend rendering approach and ultimately became our main focus. We will present our findings about the various options we have identified and elaborate in more detail on the developed print backend by presenting technical details as well as test results regarding performance, stability and quality.
#### Description
The demand for generating PDF files (e.g., for print) from online map services remains strong. Given the opportunities offered by vector tile-based maps and their resulting widespread adoption, particularly Mapbox Vector Tiles, providing this functionality in the context of web applications presents technical challenges that reveal shortcomings of existing solutions (e.g., mapfish print) and call for new approaches.\
By analyzing and testing well-established technologies, we identified several issues leading to unsatisfactory map renderings and PDF outputs. Some flaws are subtle, such as text cut-offs at tile seams, while others result in entirely unusable outputs, such as difficulties handling transparent layers.
To address these shortcomings, we developed an alternative approach by delegating both map rendering and PDF generation to a Headless Chrome instance controlled by Puppeteer, running inside a dedicated Node.js Docker container. This solution offers significant flexibility in choosing the rendering engine (e.g., MapLibre GL JS or OpenLayers) and defining the print frame (a simple HTML page with a map canvas for displaying raw maps is sufficient but can be extended with custom elements as required). \
Furthermore, by explicitly adding GPU support to the container, which is not available by default, we achieved good performance in map rendering and PDF generation while ensuring the same rendering quality as in the online map display.
#### Key Points
The primary factors motivating our choice of this setup included:
- Identical map display as online with MapLibre GL (WYSIWYG – What You See Is What You Get)
- Stability of the server even under heavy load
- Scalability on AWS server
- Maximum flexibility for layout design, defined directly in HTML
Optimizations crucial for performance:
- GPU support
- Caching
Comparative analysis of:
- Conventional services (MapFish Print)
- TileServer GL
- Frontend-based solutions (InkMap)</abstract>
<slug>foss4g-europe-2025-3480-pdf-map-generation-as-headless-chrome-service</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/JRTHPX/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/JRTHPX/feedback/</feedback_url>
</event>
<event guid="082e862d-265f-5f33-9dfe-2bef124f0721" id="3974">
<room>SA01</room>
<title>PICANTEO: a multi-modal open source change detection pipeline for reliable uncertainty-aware disaster response</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>When a natural disaster strikes, the International Charter: Space and Major Disasters [1] can be activated to deliver images of affected areas in a matter of hours or days. Currently, most rapid mapping services are based on error-prone and time-consuming annotations of photo-interpretation experts, who manually label impacted structures in order to provide maps that locate and quantify damage to local authorities and emergency services. This kind of emergency mapping is known as "Rapid Mapping" by Copernicus Emergency Management Services, and differs from "Risk and Recovery", which aims to provide more detailed assessment of destruction and reconstruction for post-disaster monitoring purposes within a time span of several weeks to months.
To support human annotators in their task, CNES (the French space agency) is developing a change detection software tool [2] that takes advantage of the fusion of very high resolution 2D and 3D satellite data (less than 70~cm resolution). This tool, called PICANTEO for ‘Photogrammetry and Imagery Change Analysis based on Neural network Toolbox for Earth Observation’, aims to identify destroyed buildings and provide accurate damage maps as quickly as possible to annotator teams. To promote its use, PICANTEO is distributed as open source on Github (github.com/CNES/picanteo).
This tool features a 2D change detection pipeline. Based on state-of-the-art deep neural networks that detect semantic changes between satellite images, and optionally a 3D change detection pipeline that confirms and completes the previous results using data fusion. This paper will explain in detail how PICANTEO integrates 2D uncertainty estimation for semantic segmentation and ambiguity estimation for 3D reconstruction to filter out false detections and improve the reliability of multimodal change detection results.
Given that the 2D modality is always available after a disaster in the case of "Rapid Mapping", priority is given to the robustness and transferability of models that are supported by reliable estimates of their uncertainty. This involves integrating advanced data augmentation techniques to create models that are resilient to a wide range of potentially degraded acquisition conditions (off-nadir, important cloud cover, etc.) while ensuring transferability between a selection of sensors. In addition to demonstrating the impact of incorporating uncertainty in the 2D change detection pipeline, this paper also includes an overview of various basic approaches for estimating uncertainty using modern deep neural network architectures. This overview includes a unified framework for comparing and evaluating these approaches in terms of model calibration and significance of derived uncertainty indicators, as well as a qualitative visualization of different uncertainty maps.
Although the availability of very high-resolution stereo acquisition is currently fairly limited from a global point of view and therefore more suited to programmed acquisitions in the case of "Risk and Recovery", new satellite constellations such as the CO3D [3] mission will greatly increase the accessibility of the 3D modality. This work can therefore also be seen as a prelude to the arrival of this new data source, in anticipation of future change detection approaches taking advantage of this additional feature. PICANTEO includes an optional 3D module to extend the 2D pipeline. It relies on the production of a Digital Surface Model (DSM) from a stereo acquisition using the open-source photogrammetry tool CARS [4]. This processing is applied to both acquisitions: before and after the disaster, and CARS incorporates a geometric refinement module that enables the DSMs to be registered together. The next step is to use Bulldozer [5] to extract the Digital Terrain Models (DTMs) from the DSMs in order to obtain the Digital Height Model (DHM) or nDSM to capture the above-ground features (building, vegetation) from the two DSMs. The point of calculating 3D change on DHMs rather than DSMs is to avoid taking into account specific 3D changes that interfere with building change detection, such as vibrations in the DSM or landslides. The XDEM tool is then used to compare the DHMs at the two dates and co-register them to correct any residual bias. Finally, the 3D pipeline incorporates filtering methods, in particular the integration of the ambiguity concept [6], which avoids interpreting correlation errors during the DSM generation step (water or shadow areas, for example) as 3D changes.
In order to simplify the user experience, a visualization portal has been implemented to enable navigation within the studied scene. Users can use this dashboard to easily browse the different layers mentioned above: 2D changes, building footprints, 2D uncertainties, 3D changes, DHM, 3D ambiguity, 2D/3D changes fused, etc. The dashboard is made up of three side-by-side panels: one for viewing the 2D layers before the disaster, a second one for the 2D post-event layers, and, if 3D data are available, a third tile displays the 3D layers. This dashboard allows users to easily navigate and assess damage after the disaster.
To evaluate the performance of our method in the context of natural disaster response, this paper will include a recent case of activation of the International Charter on Space and Major Disasters, for which the damage has been annotated.</abstract>
<slug>foss4g-europe-2025-3974-picanteo-a-multi-modal-open-source-change-detection-pipeline-for-reliable-uncertainty-aware-disaster-response</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/NDN3YK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/NDN3YK/feedback/</feedback_url>
</event>
<event guid="96ff8dc7-280a-5bd6-843b-fb6849bb3ebc" id="3393">
<room>SA01</room>
<title>Serving earth observation data with GeoServer: COG, STAC, OpenSearch and more...</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Never before have we had such a rich collection of satellite imagery available to both companies and the general public. Between missions such as Landsat 8 and Sentinels and the explosion of cubesats, as well as the free availability of worldwide data from the European Copernicus program and from Drones, a veritable flood of data is made available for everyday usage.
Managing, locating and displaying such a large volume of satellite images can be challenging. Join this presentation to learn how GeoServer can help with with that job, with real world examples, including:
- Indexing and locating images using The OpenSearch for EO and STAC protocols
Managing large volumes of satellite images, in an efficient and cost effective way, using Cloud Optimized GeoTIFFs.
- Visualize mosaics of images, creating composite with the right set of views (filtering), in the desired stacking order (color on top, most recent on top, less cloudy on top, your choice)
- Perform both small and large extractions of imagery using the WCS and WPS protocols
- Generate and view time based animations of the above mosaics, in a period of interest
- Perform band algebra operations using Jiffle
Attend this talk to get a good update on the latest GeoServer capabilities in the Earth Observation field.</abstract>
<slug>foss4g-europe-2025-3393-serving-earth-observation-data-with-geoserver-cog-stac-opensearch-and-more-</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VPNXFP/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VPNXFP/feedback/</feedback_url>
</event>
<event guid="0c65a8bb-a90e-5510-99e2-1cc26d1802ec" id="3433">
<room>SA01</room>
<title>GeoMapFish, QGIS and TEKSI Modules: A Comprehensive Open-Source Solution for Swiss Surveying Offices</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>Swiss surveying offices manage a variety of geospatial data to support public administration and infrastructure planning. Open-source solutions provide an alternative to proprietary systems, offering flexibility and interoperability. This presentation explores the combined use of GeoMapFish, QGIS, and TEKSI modules to address the specific needs of surveying offices, particularly in managing and sharing spatial data.
GeoMapFish is a WebGIS platform designed for developing interactive mapping applications. It supports multiple geospatial data sources and integrates various cartographic engines such as MapServer, QGIS Server, and GeoServer. Its architecture allows municipalities, cantons, and private organizations to visualize and distribute geodata through a web interface while maintaining fine-grained access control. In Switzerland, several entities use GeoMapFish to operate public and professional geoportals.
QGIS is widely used as a desktop GIS tool for geodata analysis, editing, and visualization. Its modular architecture enables users to customize workflows, ensuring compatibility with national and international geospatial standards. The strong open-source community behind QGIS continuously enhances its capabilities, making it a widely accepted tool for geospatial professionals.
TEKSI modules extend GIS capabilities to support public infrastructure management. These modules provide specific tools for potable water and wastewater system monitoring. The TEKSI Eau Potable module allows tracking of water supply networks, while TEKSI Assainissement facilitates wastewater infrastructure management. Built on QGIS and PostgreSQL/PostGIS, these modules offer structured workflows for data organization and decision-making.
The integration of these tools provides a structured approach for managing geospatial data in surveying offices. It supports multi-communal collaboration, enables the analysis and publication of geographic information, and enhances infrastructure management processes. This presentation will highlight concrete applications of this integrated system in Swiss surveying offices and discuss its benefits and challenges in operational contexts.</abstract>
<slug>foss4g-europe-2025-3433-geomapfish-qgis-and-teksi-modules-a-comprehensive-open-source-solution-for-swiss-surveying-offices</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/CEUSSA/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/CEUSSA/feedback/</feedback_url>
</event>
</room>
<room name="SA02" guid="65085993-f6f2-5613-95e2-cda29c59e1c5">
<event guid="0685ca73-b1cf-56d7-b342-e98da55139bc" id="3503">
<room>SA02</room>
<title>Creation of a plugin for processing geographic data in radiecology</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>SYMBIOSE is a platform principally used to simulate the transfer of radionucleides in the environment and the associated dose calculation. Its interest is notably being able to act and calculate on the specific geographical characteristics of a site: land use, population …
A plugin (Preprocessor) was created under QGis, it is characterized by:
- The possibility of creating and/or importing a grid of geographical entities on which the calculations will be made;
- Land use modeling carried out using geographic databases or satellite scenes;
- Modeling of the hydrographic network (DEM, triangulation …);
- A translation of the processed files into xml files to export the geographical entities and the different characteristics of the site (discharge points, municipalities concerned …).
Each step can be performed independently for others applications (and not specially in the context of SYMBIOSE).
Plugin created by Oslandia Society.</abstract>
<slug>foss4g-europe-2025-3503-creation-of-a-plugin-for-processing-geographic-data-in-radiecology</slug>
<track>Open Data</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/F9EF9Q/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/F9EF9Q/feedback/</feedback_url>
</event>
<event guid="2003a9c5-037b-5d1b-8e59-8bc9a7c745ba" id="3477">
<room>SA02</room>
<title>Integrating WaPLUGIN in QGIS for Downscaling WaPOR Data: Enhancing Water Resource Management</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>The FAO’s WaPOR database provides open-access data on evapotranspiration, biomass production, and water productivity, supporting sustainable water management in agriculture. However, its spatial resolution varies across levels: Level 3 data (20 m) is available only for selected irrigation schemes, while Level 1 (300 m) and Level 2 (100 m) cover broader regions but lack the detail needed for localized analysis. This limitation makes it challenging to assess water use at the field scale, particularly in areas where high-resolution data is not available.
In this presentation, we would like to demonstrate how QGIS can facilitate the downscaling of WaPOR data using machine learning techniques with high-resolution auxiliary datasets. By enhancing the spatial resolution of WaPOR data estimates, this approach provides a more detailed and accurate representation of water use across diverse landscapes. This capability is crucial for gaining a better overview of agricultural water consumption and improving irrigation management in regions beyond the predefined Level 3 coverage.
We also present our roadmap for integrating this downscaling methodology into WAPlugin, making it a user-friendly and reproducible tool within QGIS. By combining open-source GIS and data-driven techniques, this initiative strengthens the accessibility and usability of WaPOR data for researchers, water managers, and policymakers. Attendees will gain insights into the technical workflow, practical applications, and future development of WAPlugin’s new features.</abstract>
<slug>foss4g-europe-2025-3477-integrating-waplugin-in-qgis-for-downscaling-wapor-data-enhancing-water-resource-management</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/H83J7T/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/H83J7T/feedback/</feedback_url>
</event>
<event guid="6dc5d156-9358-54c2-b42b-47fe26e33bbb" id="3426">
<room>SA02</room>
<title>Giswater 4</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>Giswater is an open-source software platform available as a plugin in QGIS that bridges the GIS with relational databases for the management of water-related structures. It is specifically designed to manage water supply networks, and urban drainage systems and integrate GIS with hydraulic modelling tools like EPANET for water supply networks and SWMM for stormwater and sewer systems, enabling simulations directly from QGIS. It also incorporates inventory management through PostgreSQL Database systems.
The design and operation of urban sanitation and drainage networks have always been largely overlooked in the integral water cycle. In this sense, Geographic Information Systems (GIS) and mathematical models for networks play a key role in both the design and exploitation phases. This project demonstrates the viability of carrying out a long-term strategy for asset management of urban sanitation and drainage networks with the use of open-source technologies. In the design phase, a complete analysis of the terrain can be carried out along with sizing and selection of materials, and design of auxiliary structures of network elements. In the exploitation phase, this set of technologies working in solidarity will allow us to efficiently work on the activities and processes necessary to maintain and operate the systems efficiently and effectively. This includes preventive and corrective maintenance, monitoring and control, emergency management, or resource optimization ensuring seamless operations. This project demonstrates the possibility of carrying out all the work professionally stated with open-source technologies, which opens the door to the universalization of urban sanitation management, regardless of the degree of maturity or available capital since access to technology has become possible in this regard.</abstract>
<slug>foss4g-europe-2025-3426-giswater-4</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/ETPJKW/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/ETPJKW/feedback/</feedback_url>
</event>
<event guid="3e1d7767-57f0-5d65-ac3a-59567e32e2dd" id="3400">
<room>SA02</room>
<title>Processing and publishing Maritime AIS data with GeoServer and Databricks in Azure</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>The amount of data we have to process and publish keeps growing every day, fortunately, the infrastructure, technologies, and methodologies to handle such streams of data keep improving and maturing. GeoServer is an Open Source web service for publishing your geospatial data using industry standards for vector, raster, and mapping. It powers a number of open source projects like GeoNode and geOrchestra and it is widely used throughout the world by organizations to manage and disseminate data at scale. We integrated GeoServer with some well-known big data technologies like Kafka and Databricks, and deployed the systems in Azure cloud, to handle use cases that required near-realtime displaying of the latest AIS received data on a map as well background batch processing of historical Maritime AIS data.
This presentation will describe the architecture put in place, and the challenges that GeoSolutions had to overcome to publish big data through GeoServer OGC services (WMS, WFS, and WPS), finding the correct balance that maximized ingestion performance and visualization performance. We had to integrate with a streaming processing platform that took care of most of the processing and storing of the data in an Azure data lake that allows GeoServer to efficiently query for the latest available features, respecting all the authorization policies that were put in place. A few custom GeoServer extensions were implemented to handle the authorization complexity, the advanced styling needs, and big data integration needs.</abstract>
<slug>foss4g-europe-2025-3400-processing-and-publishing-maritime-ais-data-with-geoserver-and-databricks-in-azure</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/TTF3ME/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/TTF3ME/feedback/</feedback_url>
</event>
<event guid="d9a4b6a2-594a-5f13-8610-35855100a990" id="3287">
<room>SA02</room>
<title>Integrating Mapstore2 with QGIS Server For Printing QGIS Layouts</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>At GlobalEDA, the GIS Department has been an active representative of Open-Source GIS since 2015. Our main market is in the Azores Region, where we focus on collaborating with public entities, aiming for readily accessible and easy to understand WebGIS solutions.
This presents an unique setting for Open-Source GIS, where many public entities either don't have a WebGIS solution at all, or have committed themselves to closed-source solutions (mainly ESRI). It's also rare to find GIS technicians in these public entities who are able to use these closed-source solutions in a way that justifies their licensing fees.
We believe that MapStore2 is a very complete WebGIS solution which addresses all of the use cases necessary for these entities. With that being said, we have been using this framework to address these needs, and we've also developed our own in-house MapStore2 Plugin.
This plugin allows any user to draw a polygon on a map and have that polygon drawn on top of any number of maps in a given QGIS Layout, which are consumed, and printed, by QGIS Server. In this plugin, we use GeoServer's Filter Functions to help us interact with QGIS Server. In this talk, we're presenting you our specific implementation of this solution, which has been iterated since our last talk in QGIS UC 2024.</abstract>
<slug>foss4g-europe-2025-3287-integrating-mapstore2-with-qgis-server-for-printing-qgis-layouts</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/7W7B97/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/7W7B97/feedback/</feedback_url>
</event>
<event guid="d14243d0-07ef-55d7-83bb-db787ea3a57b" id="3413">
<room>SA02</room>
<title>Explore open-source tools for creating digital urban models with MapStore</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>The presentation describes processes and open-source tools employed by the author and his team to build and consume digital models for urban environments. The results of these processes will be rendered in MapStore as 3D Tiles layers, an OGC community standard designed for streaming and rendering massive 3D geospatial content. MapStore WebGIS framework support for 3D Tiles and glTF models through the Cesium mapping library has been greatly enhanced to support a more powerful integration. The latest versions of MapStore also include improvements and tools for exploring 3D data such as Map Views, Styling, 3D Measurements, Annotations and more.
Attendees will be presented with an overview of our work related to 3D data processing and visualization, and a selected city will be used to exemplify the processes. At the end of the presentation, attendees will be able to use the presented processes, tools and workflows to replicate them in different urban scenarios, finally visualizing them with the 3D tools of the MapStore WebGIS application.</abstract>
<slug>foss4g-europe-2025-3413-explore-open-source-tools-for-creating-digital-urban-models-with-mapstore</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/CXPGPQ/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/CXPGPQ/feedback/</feedback_url>
</event>
<event guid="d9f54c62-0c49-5eda-88f2-6bda87d3d449" id="3508">
<room>SA02</room>
<title>Spatial data Management in PostGIS</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T15:30:00+02:00</date>
<start>15:30</start>
<duration>00:30</duration>
<abstract>Got some vector data? Want to know how to manage it in PostGIS? We'll go over few tips and tricks from getting started to scaling the storage, showcasing "PostGIS is all you need"
- Basics of PostGIS - point, lines and polygons
- Indexing in PostGIS - GIST &amp; BTree
- Uber h3 indexing
- Tiles from vector data
- Here comes the temporal dimension - TimescaleDB
- Building insights at database level
- Compress your data
- Deploying strategies: managed vs self-hosted | single node vs cluster
- Next steps for highly available database with least cost
- Shall we consider MongoDB?</abstract>
<slug>foss4g-europe-2025-3508-spatial-data-management-in-postgis</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/98W8XR/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/98W8XR/feedback/</feedback_url>
</event>
<event guid="057ddf7c-ceed-5829-a378-9bdd7bcea0d3" id="3462">
<room>SA02</room>
<title>Spatial Yield processing and analysis in Bayer Crop Science's global field trials with GeoServer and PostgreSQL with PostGIS</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T16:00:00+02:00</date>
<start>16:00</start>
<duration>00:30</duration>
<abstract>In the era of precision agriculture, the ability to effectively manage and analyze spatial data is crucial for ensuring sustainable farming practices and optimizing crop yields.
This presentation will delve into the innovative use of Open Source technologies, specifically PostgreSQL with PostGIS and GeoServer, within the Spatial Yield Program in Bayer Crop Science's global field trials.
The primary objective of this project was to establish a robust data pipeline that facilitates the rapid delivery of critical information to end-users. This data is essential for assessing the quality and performance of various corn and soybean varieties, enabling agronomists and farmers to make informed decisions based on real-time insights.
By leveraging PostgreSQL and PostGIS, we harnessed the power of spatial databases
to efficiently store, manage, and query large datasets exceeding 1TB of high-precision data collected by planters and combines. Meanwhile, GeoServer enabled seamless integration and visualization of this data through web services, serving up to 75 million requests per day internally.
One of the significant technical challenges we faced was the need to ensure that the data delivery process was not only swift but also reliable, allowing users to access and analyze data without delay.Our solution involved the development of a streamlined pipeline that automates data processing and integrates multiple data sources, ensuring that users receive timely updates on crop performance metrics.
This presentation will highlight the methodologies including AWS services, Apache Kafka and K8S crons employed in the project, the challenges encountered, and the solutions implemented.
We will also discuss the impact of our work on the decision-making processes within the agricultural sector and how Open Source technologies can empower organizations to harness the full potential of spatial data.
The primary objective of the program is to assist growers in identifying the best products for their farms by rigorously testing products across diverse environments to determine the most effective solutions tailored to each specific context, as well as the management practices that yield optimal results.</abstract>
<slug>foss4g-europe-2025-3462-spatial-yield-processing-and-analysis-in-bayer-crop-science-s-global-field-trials-with-geoserver-and-postgresql-with-postgis</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/J8PKC7/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/J8PKC7/feedback/</feedback_url>
</event>
</room>
<room name="CA01" guid="d704391c-14ae-55a0-8038-46cac687da65">
<event guid="2897f842-3aaf-5d14-a897-8f5f6ed69ad4" id="4152">
<room>CA01</room>
<title>Collaborative Field Mapping in Mostar with HOT tech team - option 1</title>
<subtitle/>
<type>Full session</type>
<date>2025-07-18T09:30:00+02:00</date>
<start>09:30</start>
<duration>01:00</duration>
<abstract>Join us in these 2x 1 hour sessions (option1 in the morning and option 2 in the afternoon) to get hands-on experience using the Field Tasking Manager ( https://fmtm.hotosm.org ). We will use the imagery collected during the community drone mapping workshop on Tuesday 15 July !
You will get to collaborate with people in this fun and engaging session. You will be introduced to the Field-Tasking Manager and how to use it on your phone (first 10mins in the conference room), then you go and start the data collection process outside in the conference venue. We will give you directions on where to go. At the end of this session you will have an idea of how you can contribute as a mapper or potentially set up a project using the Field Tasking Manager.</abstract>
<slug>foss4g-europe-2025-4152-collaborative-field-mapping-in-mostar-with-hot-tech-team-option-1</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/T7RQ9P/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/T7RQ9P/feedback/</feedback_url>
</event>
<event guid="87a55e01-1dd0-5242-889e-67969aea863a" id="3506">
<room>CA01</room>
<title>Put all your data into hexagons</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>Putting all the data you have into hexagons doesn't necessarily improve the quality of your data, but it does give you a very powerful way of comparing and analyzing your datasets!
In this talk I will present how you can use postgres to turn all your spatial data into hexagons with h3-pg and how you can do lightning fast analysis with the h3 library. On top of that I will explain how you can now use vector comparison on your datasets and how well this all compresses into parquet files for sharing with the rest of the world.
This presentation has lots of small code examples (bash, sql) and pretty pictures.</abstract>
<slug>foss4g-europe-2025-3506-put-all-your-data-into-hexagons</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FYBCWF/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FYBCWF/feedback/</feedback_url>
</event>
<event guid="0284d576-6fd9-5799-a6a5-60233e7b44cb" id="3252">
<room>CA01</room>
<title>Humanitarian data collection in browser-based Postgres</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>In a humanitarian context, data collection can be divided into two main categories: proactive collection of data that may be useful for disaster response and recovery; reactive collection that is required to assess the situation on the ground during an event.
The Humanitarian OpenStreetMap Team has supported both types of mapping through the Tasking Manager platform. Going forward we will also be able to collect drone imagery collaboratively and add collected field-data to complement and match to the remote data, with the Drone TM and Field Mapping TM (FMTM) respectively.
There are often major hurdles for field-based data collection:
1. How to effectively collaborate with multiple data collectors at the same time.
2. How to work when there is poor connectivity in an area.
Web applications may be an acceptable choice to solve the first issue, but typically perform poorly when subjected to the second.
With a new paradigm in web development, local-first applications, this may no longer be an issue.
We can develop web-based applications that allow for both:
- Real-time update for users undertaking collaborative data collection campaigns.
- Fully offline data collection capability, with syncing and conflict resolution once connectivity is restored.
These capabilities have been achieved through some major landmarks over time:
- Addition of WASM to the browser in 2017.
- Implementation of databases in the web-browser (SQLite, Postgres), using WASM.
- Introduction of smart data reconciliation mechanisms such as CRDTs.
- Continual improved access to mobile phones globally, particularly in the introduction of high-performance smart phones.
This talk explores our journey implementing a local-first field mapping flow, with an example and demo to demonstrate its efficacy.</abstract>
<slug>foss4g-europe-2025-3252-humanitarian-data-collection-in-browser-based-postgres</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/QWR3DV/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/QWR3DV/feedback/</feedback_url>
</event>
<event guid="7d0ba9ab-ebfd-598a-9a83-bef56d6d63b3" id="3496">
<room>CA01</room>
<title>BioAtlas - Biodiversity Atlas of Croatia</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:30</duration>
<abstract>The BioAtlas - Biodiversity Atlas of Croatia project included the implementation of a complex information system that enables the storage, editing, viewing, downloading and sharing of field observations of animal and plant species in Croatia.
The system is implemented on the open-source platform Atlas of Living Australia (ALA) which was adapted to Croatian language and legislative. The ALA project is supported by Australian NCRIS and CSIRO and is being actively developed and maintained by the contributors organized under the Living Atlases community initiative. It is a mature open-source project that currently supports more than 20 national biodiversity portals across the world. It’s also the important part of the GBIF ecosystem which promotes and supports free, open and interoperable access to biodiversity data.
The talk will include the experiences and lessons learned during this challenging implementation of the new system in Croatia that was done in 2024 and is now in production. Talk will focus on geospatial features of the platform, its customized WMS server based on GeoServer and a wide range of spatial and statistical analysis functions.
The project has been implemented by Croatian companies VEROX and Protok for the Ministry of Environmental Protection and Green Transition.</abstract>
<slug>foss4g-europe-2025-3496-bioatlas-biodiversity-atlas-of-croatia</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/3M7GJH/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/3M7GJH/feedback/</feedback_url>
</event>
<event guid="f098efcf-3e45-5d7a-80a8-b3fad10eae9d" id="3482">
<room>CA01</room>
<title>Next-Level Vector Editing in Web GIS Applications</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>In interactive web maps, the OpenLayers library is one of the most powerful and flexible open-source libraries. The OpenLayers library in the 10th release version brings many improvements, including optimized performance when handling large vector data sets, advanced data styling capabilities including support for advanced styles and symbols, better support for rendering and data handling on mobile devices, and easier integration with modern technologies.
In this presentation, we will explore advanced tools for editing vector datasets, including improved drawing functions, precise geometry handling, and optimized interactions. Special emphasis will be placed on the ability to create custom controls on the map, which will make it easier to track changes, especially changes to selected geometries as well as several events emitted by the OpenLayes Map component. These custom events allow for more flexible and improved user interfaces.
As an example, we will show a custom toolbox that allows drawing new and editing existing geometries. The toolbox has integrated capabilities to track changes to selected geometries and this allows us to make undo-redo operations for edited geometries throughout the entire editing session.
This presentation is intended for everyone who wants to exploit the potential of new features of the current stable release of OpenLayers whether they are programmers or GIS experts.</abstract>
<slug>foss4g-europe-2025-3482-next-level-vector-editing-in-web-gis-applications</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/M8FA7A/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/M8FA7A/feedback/</feedback_url>
</event>
<event guid="4ef0f491-e466-53b8-b8a6-ac4843996c65" id="3512">
<room>CA01</room>
<title>Terra Draw: 3 Years of Open Source Map Drawing</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>Drawing on web maps can be surprisingly complex, especially when handling diverse mapping libraries and intricate user requirements. Three years ago, Terra Draw was created to simplify and standardize user drawing functionality across popular web mapping platforms, including Leaflet, OpenLayers, Google Maps, MapboxGL JS, and MapLibreGL JS. Since then, it has evolved into a robust open-source library, offering a range of built-in drawing modes for simple geometries like points, lines, and polygons, as well as advanced features like snapping, rotation, and scaling that "just work" across different mapping ecosystems.
In this talk, we’ll take a retrospective look at the development of Terra Draw—what we got right, the challenges we faced, and the lessons learned along the way. We'll also explore how the project has grown, how the community has shaped its evolution, and where it’s headed in the future. From new features on the horizon to opportunities for collaboration and expansion, this session will provide insight into what’s next for Terra Draw and how it continues to adapt to the ever-changing landscape of web mapping.
Whether you’re already using Terra Draw or just hearing about it for the first time it, this talk will provide valuable takeaways on the journey of building and maintaining an open-source project in the web mapping space.</abstract>
<slug>foss4g-europe-2025-3512-terra-draw-3-years-of-open-source-map-drawing</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/XHMYBK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/XHMYBK/feedback/</feedback_url>
</event>
<event guid="5375dcb8-f618-573c-829b-7043856fc7dd" id="3428">
<room>CA01</room>
<title>maplibre-gl-terradraw - new drawing plugin for maplibre-gl-js</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:30:00+02:00</date>
<start>14:30</start>
<duration>00:30</duration>
<abstract>This talk introduces a new drawing plugin for MapLibre: [maplibre-gl-terradraw](https://github.com/watergis/maplibre-gl-terradraw).
Historically, both Mapbox and MapLibre have relied on an older plugin, [mapbox-gl-draw](https://github.com/mapbox/mapbox-gl-draw), to provide drawing functionality in **maplibre-gl-js**. However, **mapbox-gl-draw** is no longer actively maintained, making it increasingly difficult to use with MapLibre.
As an alternative, [Terra Draw](https://github.com/JamesLMilner/terra-draw) offers advanced drawing features for multiple mapping libraries, including **Mapbox**, **MapLibre**, **OpenLayers**, **Leaflet**, **Google Maps**, and **ArcGIS**, all within a unified user interface. Compared to **mapbox-gl-draw**, **Terra Draw** is significantly easier to use. However, integrating its full functionality into MapLibre still requires extensive configuration.
To simplify this process, I developed **maplibre-gl-terradraw**, a new plugin that enables a pre-configured drawing feature with a single line of code using `map.addControl`.
With **maplibre-gl-terradraw**, users can easily add drawing controls using `map.addControl(new MaplibreTerradrawControl())`.
This immediately grants access to all drawing modes (point, line, polygon, rectangle, circle, etc) powered by **Terra Draw**. The plugin comes pre-configured with icons and additional functionalities, such as:
- Selecting and deleting features
- Downloading drawn features
- Customizing Terra Draw options and styles, as described in the documentation
Furthermore, the **measure control** allows users to:
- Measure the distance of a line or the area of a polygon
- Query elevation data from raster DEM sources (MapLibre Terrain, TerrainRGB, and Terrarium)
In this talk, I will demonstrate the core functionalities of **maplibre-gl-terradraw** and show how easily you can integrate drawing features into your **MapLibre** applications.</abstract>
<slug>foss4g-europe-2025-3428-maplibre-gl-terradraw-new-drawing-plugin-for-maplibre-gl-js</slug>
<track>State of software</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/UTUW8Z/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/UTUW8Z/feedback/</feedback_url>
</event>
<event guid="77790970-7791-5abf-95d4-e460a8b3e8ee" id="4153">
<room>CA01</room>
<title>Collaborative Field Mapping in Mostar with HOT tech team - option 2</title>
<subtitle/>
<type>Full session</type>
<date>2025-07-18T15:30:00+02:00</date>
<start>15:30</start>
<duration>01:00</duration>
<abstract>Join us in these 2x 1 hour sessions (option1 in the morning and option 2 in the afternoon) to get hands-on experience using the Field Tasking Manager ( https://fmtm.hotosm.org ). We will use the imagery collected during the community drone mapping workshop on Tuesday 15 July !
You will get to collaborate with people in this fun and engaging session. You will be introduced to the Field-Tasking Manager and how to use it on your phone (first 10mins in the conference room), then you go and start the data collection process outside in the conference venue. We will give you directions on where to go. At the end of this session you will have an idea of how you can contribute as a mapper or potentially set up a project using the Field Tasking Manager.</abstract>
<slug>foss4g-europe-2025-4153-collaborative-field-mapping-in-mostar-with-hot-tech-team-option-2</slug>
<track>Use cases &amp; applications</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VRAJXV/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VRAJXV/feedback/</feedback_url>
</event>
</room>
<room name="PA01" guid="16fc454c-4a03-597f-99ce-1866d0ded964">
<event guid="abf774ad-2ede-5022-a353-27f5975f3785" id="3978">
<room>PA01</room>
<title>Real Time Co-editing of High Vertex Count Geometries Using OpenLayers and CRDTs - a Performance Analysis</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:00:00+02:00</date>
<start>11:00</start>
<duration>00:30</duration>
<abstract>Real-time GIS has become an essential tool in various domains, including Volunteered Geographic Information (VGI) and disaster management. One of critical topics in real-time GIS is concurrency control (Sun and Li 2016). In the geospatial domain, concurrency has traditionally been controlled using optimistic or pessimistic models (i.e. versioning and locking, respectively). In the domain of distributed databases, a standard consistency model, called strong consistency, ensures that a set of distributed databases behave as if they were a single database. However, enforcing strong consistency can introduce bottlenecks and lags, and requires significant hardware resources and time to implement.
To address these drawbacks, a more relaxed approach called strong eventual consistency (SEC) (Gomes et al. 2017) has been developed in the domain of real-time text co-editing. Unlike strong consistency, SEC lets each site edit its local copy of the data without any restrictions and replicate all the updates to all other sites, which, upon reception, apply them on their local data. Temporary local inconsistencies are allowed between the participating sites, but it is guaranteed that, once all sites have received the same set of updates, they will be in the same state (i.e., they will converge).
It has recently been shown that SEC model i.e. its instantiation, CRDT (Commutative Replicated Data Type) technology (Shapiro et al. 2011) can also be used for the task of geospatial co-editing. Within the research (Matijević et al. 2024), an experimental real-time geospatial co-editor (source code referenced in the original paper) has been developed and tested. The implementation uses OpenLayers (OL) on the graphical user interface (GUI) and a small, unoptimized but complete and correct Javascript CRDT library called Reference CRDTs (Gentle 2023). The research showed that, when applied to the co-editing of geospatial geometry in its native form, standard CRDT conflict resolution mechanics exhibit some issues. As an attempt to address these issues, the authors developed an advanced operation generation technique named “tentative operations”. This technique allows for the operations to be generated over the most recent session-wide state of the data, which in effect highly reduces concurrency and provides a “geometry aware” conflict resolution. The tests conducted using the developed system showed that in low-latency network conditions, the negative effects of standard CRDT conflict resolution mechanics do get minimized even under increased loads (many concurrent sites co-editing a low vertex count geometry).
Real-time co-editors generally aim to provide good user experience of the system, with correct handling of conflicts being one of its important aspects. However, besides correct handling of conflicts, the system also has to be responsive. The responsiveness of real-time co-editors depends on the efficiency of the underlying business logic but also on the efficiency of the GUI itself. Especially in the case of geospatial data manipulation, both the GUI and the business logic will be additionally stressed by increasing the vertex count of geometries being co-edited. Since within the original research the tests were performed using polygons low vertex count (several hundreds), it remained unknown how would a CRDT based geospatial co-editor behave when much larger geometries (e.g. hundreds of thousands of vertices) are co-edited.
Within this research we therefore investigate the impact that the increase of vertex count has on the overall performance of the system, which in turn can hinder responsiveness. We reused the existing implementation from (Matijević et al. 2024) and only introduced minor modifications to achieve the ability to time the execution of its key mechanisms. Instead of focusing on the performance of CRDT mechanisms only, we observed the behaviour of the complete system. We first analysed the overall composition of the system and divided it into several distinct parts. Then, during load testing we observed how the performance of each of those parts changes when handling geometries with increasing vertex counts. To stress the system, we created three polygons with 100K, 200K and 300K vertexes and conducted real co-editing sessions over those polygons. The upper limit of 300K was selected based on preliminary testing which showed that already at this vertex count OL starts to lose responsiveness in editing mode. The sessions were conducted using several identical instances of some recent, mainstream hardware and the latest Google Chrome browser.
As expected, the results of the experiment show that the overall performance of the system does suffer from the increased vertex count of the geometry being co-edited. In the setting as implemented within the research (Matijević et al. 2024) the part most affected by the increased vertex count turned out to be the redrawing on GUI upon integration of remote updates while the client is in editing mode. This is because the implementation uses a simple redraw method where the complete geometry is replaced by its new version.
The results from this research show that geometries with high vertex count can efficiently be co-edited in real-time. However, there exists a limit where the increased vertex count will start to render the system irresponsive, depending on the capabilities of the hardware that the clients are running on.
The results from this research are to be observed in the setting which includes both CRDT and OL written in Javascript, with CRDT implementation including only some simple optimizations and with no tighter CRDT-OL coupling other than what OL offers off-the-shelf. It can therefore be expected that a deeper integration of CRDT mechanisms with OL could make some of the critical parts of the implementation faster. Also, there exist several mature, production ready open-source CRDT frameworks which include many optimizations and can outperform the Reference CRDTs by orders of magnitude.</abstract>
<slug>foss4g-europe-2025-3978-real-time-co-editing-of-high-vertex-count-geometries-using-openlayers-and-crdts-a-performance-analysis</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/NH7Y8G/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/NH7Y8G/feedback/</feedback_url>
</event>
<event guid="3643861c-0724-5a7b-980d-a73ac277c202" id="3972">
<room>PA01</room>
<title>Evaluating Matrix Factorization Techniques for Thematic Mapping of Wilderness Walkability Using Multiple GPX Datasets</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T11:30:00+02:00</date>
<start>11:30</start>
<duration>00:30</duration>
<abstract>Quantitative thematic mapping is widely used in meteorology (e.g., weather maps), geology (e.g., topographic maps), and environmental science (e.g., pollution distribution). However, mapping continuous quantitative data is challenging, especially when data is sparse or unreliable. Sparse data arises from uneven measurement distribution, while unreliable data stems from inconsistencies or errors, such as subjective self-reports or satellite-derived estimates. These challenges intensify when mapping hidden variables requiring indirect proxies[Ervin, 2009], introducing further uncertainty. Advanced techniques for integrating multiple datasets and statistical methods like interpolation or machine learning are essential for improving accuracy.
Mapping walkability and fire risk in wilderness areas is particularly difficult due to the hidden nature of these variables and data limitations. Wilderness walkability depends on factors such as trail connectivity, slope, surface quality, and accessibility, which are difficult to measure comprehensively. Desktop analyses often miss real-world obstacles like debris or vegetation overgrowth. Similarly, fire risk is influenced by vegetation dryness, wind patterns, topography, and human activity—complex interactions that are hard to quantify due to sparse sensor coverage and environmental variability. Both require indirect proxies, such as fire behavior data or trail condition audits, which introduce inaccuracies. Addressing these challenges demands advanced mapping techniques that integrate multiple data sources, including crowdsourced trail reviews, IoT sensors, and remote sensing data.
This study focuses on thematic mapping of walkability. Unlike urban walkability[Horak,2022], which is linked to built infrastructure, wilderness walkability depends on natural terrain features such as slope, surface stability, vegetation density, and trail connectivity. Measuring these factors directly is difficult due to terrain heterogeneity and dynamics. For instance, steep inclines and loose surfaces can impede movement, while dense undergrowth or debris can block trails entirely.
Walkability can be assessed using GPX trail data by calculating walking speed along trails, providing an objective measure of terrain difficulty. GPX files contain time-stamped geographic coordinates that allow speed calculations based on distance and time. However, individual differences in fitness, experience, and preferences introduce subjectivity when expressing walkability as walking speed. One hiker may struggle on rocky trails, while another navigates them with ease. Aggregating data from multiple users helps mitigate these biases, capturing broader patterns and providing a more accurate walkability representation. Walking speed alone is insufficient for defining inherent walkability, so we propose matrix factorization as a technique for revealing latent walkability values. Using multiple GPX trails, we evaluate different matrix factorization methods for thematic mapping.
Data
We collected 1,620 GPX trails from users across Croatia, including mountain rescue teams, hikers, runners, dog walkers, and casual users. To ensure anonymity, each GPX file was assigned a unique user ID without personal information. Each trail contained geographic coordinates and timestamps, though variations in recording instruments led to differences in segment lengths. Movement speed was calculated by comparing time and location of neighboring segments. After filtering out outliers, we obtained 1,795,663 valid segments described by location, time, user ID, and speed.
To address inconsistencies, segments were grouped into 100-meter spatial cells per user. The median movement speed per user-cell combination was computed, resulting in 127,478 user-cell speed descriptions. The final dataset was structured as a 1,609 × 24,349 sparse matrix, where rows represent users and columns represent terrain cells, with values indicating median walking speed.
Methods
When factorizing user-item rating matrices, various techniques uncover latent features and improve predictions (Khalitov,2021; Du,2023).
Singular Value Decomposition (SVD) factorizes the matrix into three components, capturing latent relationships through eigenvalue calculations. Truncated SVD retains only the top k singular values, primarily for dimensionality reduction in preprocessing. Non-Negative Matrix Factorization (NMF) , similar to SVD, enforces non-negative components, making results more interpretable by representing additive user-item interactions. For large datasets, Stochastic Gradient Descent (SGD) iteratively updates latent factors to minimize prediction error, while Alternating Least Squares (ALS) optimizes user and item factors in a least-squares framework. Fast Independent Component Analysis (FastICA) extracts statistically independent latent factors, assuming non-Gaussian distributions, and is mainly used for feature extraction and preprocessing.
Evaluation
All factorization techniques were tested on the dataset using Python, scikit-learn, and custom implementations. Factorization extracted a single latent factor per user and per cell. Performance was evaluated by calculating RMSE between reconstructed values (user-cell latent factor product) and original sensed values.
A cell's latent factor represents its inferred walkability. Walkability maps generated from each technique were compared with satellite imagery, topography, and land cover data using GRASS GIS statistical tools.
Results
RMSE obtained for the constructed dataset evaluation was 0.4148 (NMF and TruncatedSVD) , 0.4665 (SVD) , 0.4666 ( FastICA), 2.2839 (ALS) and 5.2349 (SGD)
Conclusion
Results confirm that matrix factorization effectively separates user and terrain latent data in sparse datasets. NMF, with its explainability, proves particularly useful for mapping hidden values, as it ensures non-negative components that directly relate to real-world factors influencing walkability. This property makes it especially suited for modeling walkability.
Extracted latent factors provide insights into spatial walkability patterns, revealing areas where walking conditions are more or less favorable. These factors can serve as a foundation for extrapolating walkability across larger areas using geospatial datasets, including land cover classifications, slope gradients, and aspect orientations. Additionally, integrating these results with external environmental datasets could lead to predictive models for walkability in natural landscapes. Future research should explore method transferability across diverse geographical regions and other applications, such as fire risk mapping, where fire behavior data could quantify fire susceptibility.</abstract>
<slug>foss4g-europe-2025-3972-evaluating-matrix-factorization-techniques-for-thematic-mapping-of-wilderness-walkability-using-multiple-gpx-datasets</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/MTM3ZA/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/MTM3ZA/feedback/</feedback_url>
</event>
<event guid="e43b610e-1ddd-5b3a-8353-5a25f5ef96c8" id="3989">
<room>PA01</room>
<title>An Open-Source WebGIS Approach to Empower Glacier Research with Scalability and Reproducibility</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-18T12:00:00+02:00</date>
<start>12:00</start>
<duration>00:05</duration>
<abstract>Glacier monitoring is essential for understanding climate change, especially for smaller glaciers, which are highly sensitive to warming and face rapid losses. The GlaMBIE team (2025) provides a global assessment of glacier mass changes from 2000 to 2023, revealing an alarming acceleration in mass loss, particularly in regions with small glacier areas. Central Europe, including the Alps, experienced the most dramatic relative loss (-39%), highlighting the vulnerability of mid-latitude glaciers. Despite their relatively small size, these ice bodies play a crucial role in regional water resources and ecosystem stability. Hugonnet et al. (2021) further quantify this acceleration, reporting a global glacier mass loss rate of 267 gigatonnes per year between 2000 and 2019, exceeding the combined ice loss of Greenland and Antarctica. Smaller glaciers, such as those in the Alps, are particularly challenging to monitor due to their fragmented nature and steep, complex terrain, which often limits data coverage and increases uncertainty. High-resolution Digital Elevation Models, such as those provided by the Pléiades Glacier Observatory, provide critical insight into these changes (Berthier et al., 2024). Given their importance for water supply and natural hazard management, maintaining long-term, high-precision monitoring and data management systems - especially through accessible platforms such as WebGIS - is crucial to reduce observational uncertainties and inform adaptation strategies (Gärtner-Roer et al., 2022).
WebGIS platforms are essential tools for environmental monitoring, enabling real-time data collection, analysis, and visualization. By integrating diverse geospatial datasets, they provide interactive assessments of environmental conditions, support change detection, and enhance decision-making. Advances in cloud computing, open-source software, and mobile technologies further improve their efficiency (Kipkemboi et al., 2023). Their ability to manage large datasets and present user-friendly visualizations has expanded their role in various environmental domains (Toro Herrera et al., 2021). In water resource management, a WebGIS environment facilitates real-time monitoring of parameters such as chlorophyll-a concentration, suspended matter, and surface water temperature, aiding awareness and policymaking (Oxoli et al., 2020). Similarly, in glacier monitoring, initiatives are emerging to integrate geological, remote sensing, and geophysical data into centralized WebGIS platforms (Senger et al., 2021). These systems address challenges posed by harsh environments and accessibility issues, enabling data sharing and visualization to improve research and fieldwork efficiency. They also support educational activities by documenting data acquisition workflows. Despite challenges such as high hardware costs and limited interpretation tools, the benefits—extended field seasons, quantitative analysis, and increased accessibility—outweigh these limitations.
This work presents a WebGIS platform designed to facilitate the exploration and analysis of the Belvedere Glacier monitoring data. The glacier, located in the Italian Alps, is the site of a long-term monitoring project coordinated by the Department of Civil and Environmental Engineering of Politecnico di Milano that yearly organises a Summer School for groups of students that are involved in the Global Navigation Satellite System measurements of documented targets distributed along the surface. This allows the derivation of velocity and volume variations over the last decade (Gaspari et al., 2024). By integrating geospatial visualization and interactive data analysis, the WebGIS platform offers an intuitive environment for researchers, environmental agencies, and stakeholders. It leverages CesiumJS for dynamic 3D geovisualization and PostgreSQL/PostGIS for spatial data management, ensuring scalability and efficiency in handling large datasets but also providing a compatible interface for flexible mobile field mapping activities employing Qfield or Mergin Maps.
The WebGIS platform is conceived as a dynamic tool intended to help monitor the Belvedere Glacier and to serve a broad audience with various needs. The motivation behind developing the platform stems from the need to improve data accessibility and usability in the context of glacier monitoring. Traditional methods of data management are often based on static files like spreadsheets and PDFs, and are proved to be inefficient, fragmented, and challenging to update. The implementation of the Belvedere WebGIS platform involved translating conceptual designs into a fully functional web application. This process included setting up a Django framework, configuring the database, developing backend logic and integrating frontend visualization tools. Key features include an interactive map, temporal data visualization, and graph-based analysis, enabling users to track glacier changes over time and examine displacement trends. The platform supports data uploads and exports, enhancing its role as a comprehensive tool for scientific research and decision-making.
Built with open-source geospatial technologies, this WebGIS provides a solid foundation for future enhancements, such as integrating orthophotos, 3D representations, as well as advanced visualization. By combining GIS technology with web-based accessibility, the platform contributes to a deeper understanding of glacier dynamics, supports data-driven environmental assessments, and serves as a valuable tool for the scientific community studying climate-driven glacier evolution.
Source code: https://github.com/labmgf-polimi/belvedere-webgis
Publication of the webGIS on a public server is in progress.
References:
The GlaMBIE Team (2025). Community estimate of global glacier mass changes from 2000 to 2023. Nature, 1-7.
Hugonnet et al. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature 592, 726–731
Berthier et al. (2024). The Pléiades Glacier Observatory: high-resolution digital elevation models and ortho-imagery to monitor glacier change. The Cryosphere, 18(12), 5551-5571.
Gärtner-Roer et al. (2019). Worldwide assessment of national glacier monitoring and future perspectives. Mountain Research and Development, 39(2), A1-A11.
Kipkemboi et al. (2023). Development of a Web-GIS Platform for Environmental Monitoring and Conservation of the Muringato Catchment in Kenya, Journal of Geovisualization and Spatial Analysis, vol. 7, no. 1.
Toro Herrera et al. (2021). A collaborative platform for water quality monitoring: SIMILE WebGIS, in Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., 201–207.
Senger et al. (2021). Using digital outcrops to make the high Arctic more accessible through the Svalbox database. Journal of Geoscience Education, 69(2), 123-137.
Gaspari et al. (2024). Bridging geomatics theory to real-world applications in alpine surveys through an innovative summer school teaching program, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W12-2024, 59–66</abstract>
<slug>foss4g-europe-2025-3989-an-open-source-webgis-approach-to-empower-glacier-research-with-scalability-and-reproducibility</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/WZ3VJB/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/WZ3VJB/feedback/</feedback_url>
</event>
<event guid="d35940a2-531e-5cba-90dc-8f9f2e1f26ff" id="3995">
<room>PA01</room>
<title>Assessing built-up surface dynamics in the Ticino River Basin using multi-source LU/LC datasets: A preliminary comparative study within the INTERREG WINCA4TI project</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-18T12:05:00+02:00</date>
<start>12:05</start>
<duration>00:05</duration>
<abstract>The management of protected areas, which are of particular importance within the Ticino River basin both in terms of biodiversity protection and enhancement and in reducing major sources of pollution, is inextricably linked to the sustainable management of water resources. This resource has become even more precious considering ongoing and anticipated climate change.
Prolonged drought periods raise a series of questions regarding the use of water resources for the various environmental and socio economic sectors of the entire transboundary Ticino hydrological system. These concerns cannot be separated from the need to safeguard natural capital, making water allocation a critical issue that requires a transboundary governance approach to ensure a balanced management of the entire Ticino basin. Therefore, the activities of this project aim to analyse, understand, and describe the complex interactions between water, economy, environment, and agriculture within the Ticino River basin. This is particularly relevant in the context of climate change, where water availability is subject to increasing variability, both excess and shortage, compared to past century values.
This project, funded by the INTERREG programme VI-A between Italy and Switzerland CCI 2021TC16RFCB033 with project id 0200112, will help identify potential risks, opportunities, and challenges for the transboundary Ticino basin in terms of policies, management, and water-related technologies. Consequently, it will address issues concerning the environment, biodiversity, ecosystems, pollution, and the socio-economic framework by identifying key challenges, developing, and proposing strategies that can be adopted across the entire transboundary region. These strategies will support a common approach to governance, management, and efficient use of water resources within the Ticino basin, paving the way for climate adaptation and the protection of natural capital and biodiversity through participatory approaches and the use of open, flexible, and sustainable techniques and technologies. The innovative and jointly developed solutions fall within the following intervention areas of the program: promoting nature-based solutions and water resource management in both irrigation and lake environments to improve environmental quality and quality of life; installing new technological tools and/or developing small infrastructures; and developing shared monitoring and data exchange systems, including information platforms and other digital exchange systems.
Additionally, actions to enhance environmental sustainability and resilience will be studied. Nature-based solutions will be developed through pilot areas to foster biodiversity, improve the microclimate, increase water storage via wetlands, and support pollinating insects. Research will also focus on optimizing water resource management by integrating smart, remotely controlled irrigation systems and a sensor network to monitor availability in real time, ensuring efficiency in the face of climate change. Therefore, the project will enhance lake ecosystem quality and biodiversity by improving existing monitoring systems.
These actions will not be developed in isolation but rather in synergy with each other and with project partners. They will also incorporate Citizen Science practices through the creation of communities of practice and learning, open to local stakeholders. The approach considers the hydrological cycle and the water’s journey from upstream to downstream in the transboundary Ticino territory, an interconnected system that enables the development of green areas, ecosystems, agricultural zones, renewable energy production, and related economic activities.
The implemented solutions will be integrated and valorised in a digital story telling WebGIS application that using open standards will manage data and produce publicly accessible information on the state of the three developed solutions and the challenges threatening the Ticino water system. OpenLayers, Geoserver and istSOS4 will be the FOSS4G technologies at the core of this application to make data FAIR using mainly the OGC standards WMS and SensorThings API. In addition to the software tools, the open-source paradigm will also be applied to implement an open datalogger designed to collect data from different sensor providing valuable insights into future project actions and climate change impacts. This open approach will allow the integration of data collected from previous project (e.g. INTERREG SIMILE) as well as from existing networks with new sensors installed within this project, field activities, and information and feedback gathered from stakeholders during public meetings planned as part of the initiative.
The ongoing dialogue among different stakeholders and associated partners of the transboundary Ticino basin will create a comprehensive and in-depth understanding of needs, challenges, and opportunities. It will also enable the initiation of actions and measures to address these needs and overcome emerging challenges, ultimately strengthening climate risk mitigation capacity through transboundary collaboration. Additionally, it will integrate climate change adaptation measures into policies, strategies, and regional planning while improving education and fostering greater awareness.
Finally, the project also aims to develop coordinated training and educational activities to raise awareness among policymakers and local decision-makers about implementing transboundary strategies for the sustainable protection and enhancement of the alpine and pre-alpine environment’s attractiveness. This approach will also be transferable to other regions, serving as a key element in maintaining the sustainable use of land and water resources.</abstract>
<slug>foss4g-europe-2025-3995-assessing-built-up-surface-dynamics-in-the-ticino-river-basin-using-multi-source-lu-lc-datasets-a-preliminary-comparative-study-within-the-interreg-winca4ti-project</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/Z3D99H/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/Z3D99H/feedback/</feedback_url>
</event>
<event guid="4e3c02bd-ecfd-52d8-b438-6c0ac0b92178" id="3985">
<room>PA01</room>
<title>Enhancing Water Resources Management with Open-Source Remote Sensing: Flood Mapping and Climate Change Insights on Kupa River case area</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-18T12:10:00+02:00</date>
<start>12:10</start>
<duration>00:05</duration>
<abstract>Flooding is one of the most damaging natural disasters, intensified by climate change, urban development, and land-use changes. Effective flood monitoring and management are crucial to mitigating the negative impacts, especially in regions with complex hydrological dynamics. This study focuses on the Kupa River basin in Croatia, a flood-prone region, and presents an integrated approach for flood mapping and climate impact assessment using open-source Earth observation (EO) data and free tools. Combining a different remote sensing datasets; Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Normalized Difference Vegetation Index (NDVI), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) precipitation datasets, all accessed through Google Earth Engine (GEE), this research demonstrates a cost-effective and scalable solution for monitoring flood dynamics and climate change insights on a focused area. Sentinel-1 SAR, with its cloud-penetrating capabilities, is used to detect surface water changes, while Sentinel-2 through the NDVI complemented vegetation health before and after flood events. CHIRPS data, with daily precipitation estimates, contextualizes the meteorological conditions that contribute to flooding. The integration of these datasets offers a comprehensive analysis of flood events and their environmental impacts, providing actionable insights for local flood management and climate change adaptation. The use of open-access and freely available data and free tools highlights the potential for replicable flood monitoring in regions with limited infrastructure, further supporting the development of early-warning systems and informed decision-making.</abstract>
<slug>foss4g-europe-2025-3985-enhancing-water-resources-management-with-open-source-remote-sensing-flood-mapping-and-climate-change-insights-on-kupa-river-case-area</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>true</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/FXCFUK/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/FXCFUK/feedback/</feedback_url>
</event>
<event guid="98dc30c7-448e-524b-905b-08b060f86c23" id="3986">
<room>PA01</room>
<title>The Role of Open Source Data in Disaster Preparedness and Response: A Case Study on Flood Impact in Local Communities</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-18T12:15:00+02:00</date>
<start>12:15</start>
<duration>00:05</duration>
<abstract>Flood is one of the most devastating natural hazards, imposing enormous social, economic, and infrastructural impacts on nations worldwide. Major flood events disrupt communities by damaging critical infrastructure, displacing large populations, and causing extensive financial losses. These disasters not only strain emergency response systems but also hinder long-term development, emphasizing the urgent need for reliable, timely, and detailed spatial data to guide both immediate action and future mitigation planning. Flood disasters often expose critical gaps in the availability of timely and accurate geospatial data.
This study investigates the role of open source data in enhancing disaster preparedness and response, with a particular focus on community-mapped data, using Jakande housing estate, 1st gate Platinum Wy Lekki Penninsula II Lekki 106104, presently located in the Eti-Osa Local government of Lagos State as a case study. By harnessing the power of volunteered geographic information contributed by local residents, the research addresses critical data gaps in under-mapped regions (Goodchild, 2007; Haworth &amp; Bruce, 2015). Community mapping initiatives not only provide timely, real‐time updates during flood events but also incorporate localized insights that traditional mapping methods may overlook. This participatory approach enriches the spatial dataset, offering details on flood extents, infrastructure damage, and population displacement. In integrating these community-driven datasets with advanced geospatial tools, the study demonstrates a significant improvement in situational awareness, ultimately supporting more informed and effective decision-making during emergency response efforts.
The methodology comprises a multi-tiered approach. Initially, high-resolution satellite imagery of the case study area was acquired over a six-year period, enabling a temporal analysis of land cover changes and pre- and post-flood conditions. This remote sensing phase provided an extensive visual record that served as a baseline for further spatial analysis. A review of existing OpenStreetMap (OSM) data revealed that the targeted area was largely unmapped—a gap that hindered the region’s disaster response capacity (Herfort et al., 2021). In response, a dedicated mapping task was initiated using the Humanitarian OpenStreetMap Team (HOT) Tasking Manager to invite contributions from volunteer mappers, thereby creating an up-to-date geographic dataset. Furthermore, the presented methodology offers insights into how to verify OSM data and contribute to the improvement of its accuracy and thoroughness.
To augment the remote mapping effort, on-ground data collection was undertaken using Open Data Kit (ODK). Field surveys focused on gathering real-time information on the condition of local infrastructure and documenting patterns of population displacement due to flooding. The data collection process incorporated stringent quality checks by cross-referencing field findings with local community insights, ensuring both accuracy and contextual relevance. This integrated approach highlights the synergy between remote sensing, open-sourced mapping, and community-based data acquisition—a combination increasingly recognized as critical for effective disaster management.
Subsequent spatial analysis was performed using QGIS, First, a multi-temporal change detection algorithm was applied by comparing classified satellite imagery from pre- and post-flood periods. This method, which utilized indices such as the Normalized Difference Vegetation Index (NDVI), allowed us to assess changes in land cover dynamics over time. Overlay analysis was then performed by intersecting the delineated flood extent polygons with mapped infrastructure layers—including residential, commercial, and roads—to pinpoint areas where vulnerable structures were concentrated. Additionally, spatial queries, including buffer and proximity analysis, were executed to delineate high-risk zones where flood extents and population clusters overlapped which allowed for a comprehensive assessment of flood-induced damage, identification of vulnerable infrastructure, and quantification of displacement metrics and to provide a clearer picture of the flood’s spatial extent. The spatial analysis outputs were visualized as detailed maps that conveyed spatial patterns and risk zones to emergency responders and policymakers.
The spatial analysis revealed that the flood inundated a vast portion of the study area, with a significant concentration of affected housing and critical infrastructure. Detailed overlay analysis showed that more than 40% of the mapped residential zones were located in high-risk flood areas. In addition, clusters of commercial and public service facilities—such as police stations—were clearly delineated within these zones, many of which had not been previously mapped in OpenStreetMap. This data gap highlighted the urgent need for community-driven mapping, which was addressed through a dedicated task via the Humanitarian OpenStreetMap Team. These findings provide crucial guidance for targeted emergency response and infrastructure reinforcement (Rajabifard et al., 2004). However, the study faced limitations, including variability in data resolution and gaps in real-time field verification. Notably, attempts to obtain Sentinel-2 satellite imagery from NASA for the study area were unsuccessful, limiting the spectral analysis capabilities. Future research should focus on integrating higher resolution satellite imagery and advanced predictive modeling to further refine flood impact assessments and enhance the overall effectiveness of disaster management strategies.
The research underscorse the transformative role of open source geodata in disaster response. By integrating satellite imagery with OSM-derived mapping, the study not only fills important data gaps but also enables rapid situational awareness during and after flood events (Grippa et al., 2022). The participatory mapping approach—combining remote sensing with field-collected data—proved to be an effective model for generating reliable spatial information in data-scarce environments. This framework demonstrates that the use of free and open-source geospatial tools can enhance both immediate response and long-term resilience planning in communities affected by natural hazards. The results of the study provide practical insights into the integration of diverse data sources for improved emergency management and suggest that such methodologies can be readily adapted to address various types of natural hazards. Future work should focus on refining these methods and exploring additional data fusion techniques to further enhance the effectiveness of disaster management strategies.</abstract>
<slug>foss4g-europe-2025-3986-the-role-of-open-source-data-in-disaster-preparedness-and-response-a-case-study-on-flood-impact-in-local-communities</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/VWG7HU/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/VWG7HU/feedback/</feedback_url>
</event>
<event guid="5962807a-c25d-5e27-8a68-555bbe3e05f2" id="3993">
<room>PA01</room>
<title>Analysis of the electric vehicle charging station coverage in Italian alpine region.</title>
<subtitle/>
<type>Lighting talk</type>
<date>2025-07-18T12:20:00+02:00</date>
<start>12:20</start>
<duration>00:05</duration>
<abstract>The transition towards more sustainable transport together with a worldwide push for decarbonization promotes the adoption of light-duty electric vehicles (EVs). Nevertheless, for EVs to run on par with or better than internal combustion engine vehicles, they require convenient enough charging infrastructure (Knez et al., 2019). EV charging infrastructure must accommodate shifting demands in terms of density (queuing), frequency (coverage gaps), and dependability (outage) (Hanig et al., 2025). Even if only a small fraction of all car trips are longer than 50 miles (well within the range of today's EVs), long-distance drivers' concerns about charging tend to have a disproportionate effect on their decision to buy a car (Haidar et al., 2022). Moreover, changing stations availability can be critical when choosing a turistic destination.
This research project analyzes the availability of EV charging stations in the Provincia Autonoma di Trento (PAT), a region in the Italian eastern Alps, a popular touristic destination for Italians and northern Europeans.
While an Italian national repository, PUN, "Piattaforma Unica Nazionale dei punti di ricarica per i veicoli elettrici" of the Ministero dell'Ambiente e della Sicurezza Energetica (Single National Platform for Charging Points for Electric Vehicles of the Italian Ministry of Environment and Energy Security) is available for consultation, its dataset cannot be downloaded as a map or a table for processing. Therefore the Open Charge Map dataset, available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) license, has been used. While this charging points database is far from complete, it is fairly representative of the distribution and density of the charging stations. The JSON dataset for Italy has been converted to CSV and the points within the Provincia Autonoma di Trento have been extracted.
The road network has been provided by the local government, Provincia Autonoma di Trento, with a 1:10000 scale, again under the CC-BY 4.0 license. Only the paved roads have been used.
The road network and the charging stations have been combined, placing a node in each station, at the each road intersection and on each road extremity.
With this configuration, the distance of each road to the closest charging station, defined as the minimum distance of the starting or ending node of the arc representing the road, has been evaluated: the minimum distance is below 1 km for most of the roads, with only a few roads above 7 km.
To provide a better representation of the distance between charging stations and potential users a set of points has been created along the roads with a distance of 500m. The distance to charging points has been evaluated for these 8975 points. Nodes belonging to roads shorter than 100m have been removed because they would have too mach influence on the distance distribution.
The mean distance from the charging points is 4749.4 m, with a standard deviation of 4592.6 m. The maximum distance of 36766.6 m, and, as expected the minimum is 100 m. Only 3161 (35.22%) points have a distance above 5 km and 1104 (12.30 %) above 10 km.
To analyze the distribution of the charging stations their density has been evaluated by extracting the charging points for each municipality. The province has 166 municipalities, ranging from relatively large cities in the main valleys to very small municipalities in secondary valleys.
The number of charging stations per municipality is quite low, 1.9 on average, but 72 (43.4%) municipalities have no charging points at all. For the other 94 (56.6%) municipalities which do have at least one charging station, the average number is of 3.32 charging point per municipality, with a standard deviation of 3.79.
Results are compatible with a recent Italian national report (MOTUS-E, 2025) indicating that more than 40% of the municipalities have no charging stations. Moreover, around 30% of Italy has a distance to the nearest charging station above 5 km, 6% above 10 km. However, results are not really comparable because the national report does not employ network analysis but a coarse raster analysis with 1 km resolution and, more importantly, it takes advantage of the access to a more complete charging stations dataset.
Future developments include the repetition of the analysis for other Italian regions, the differentiation of the analysis per types of EV chargers and the use of a more comprehensive charging stations dataset. The availability of traffic data is being investigated since it would make it possible to verify whether the charging stations distribution match the traffic distribution or it is possible to optimize its configuration to serve the largest number of vehicles.
The main limitations of the analysis come not from the processing tools but from the insufficient availability of data, which are often in fragmented, proprietary and inaccessible datasets.
All analyses and statistical and spatial processing were carried out using only FOSS, demonstrating the power and versatility of these software tools. In particular, topological analysis has been implemented using python with numpy and geopandas for data processing and igraph for network analysis. The Matplotlib library has been used for data visualization. QGIS has been used for coordinate conversion, map representation, table processing and geoprocessing.</abstract>
<slug>foss4g-europe-2025-3993-analysis-of-the-electric-vehicle-charging-station-coverage-in-italian-alpine-region-</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/E9Z8SW/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/E9Z8SW/feedback/</feedback_url>
</event>
<event guid="bdf362cf-af41-5abd-853d-694596e83c56" id="3982">
<room>PA01</room>
<title>Open Technologies Supporting Linked Open Data Publishing: Croatian Population Census Case Study</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T13:30:00+02:00</date>
<start>13:30</start>
<duration>00:30</duration>
<abstract>Population census is one of the most complex statistical undertake of a country that results in a detailed social, demographic, and economic data about its population. Achieving and maintaining of population's welfare relies heavily on effective socio-economic policies that are rooted in census data. According to the United Nations The 2020 World Population and Housing Census Programme, census data is the backbone for formulating, implementing, and monitoring of such policies (United Nations Statistical Commision, 2015 ) as it allows policymakers to make data-driven decision-making and target economic and social challenges more effectively.
In European context, the collection of census data has a long-standing tradition and today is governed by legal frameworks such as EU Regulation on Population and Housing Censuses 763/2008 that standardized methodology and comparability across countries. However, the way census data is disseminated was transformed with the adoption of 2019 EU Open Data Directive which obliged governments to unrestrictedly publish data for anyone to reuse. Not only does it argue for free and available data, but the Directive also classifies census data as high-value data. Such classification underscores its immense potential for fostering societal and economic development and urges its provision in machine readable formats or via suitable APIs to foster this goals.
Publishing census as open data on the web undoubtedly improves data accessibility but simply making the data available does not necessarily eliminate the challenges associated with data integration and interoperability. Potential solution to this problem lies in the shifting from a web of documents, which is inherently designed for human consumption, to web of data where structured, machine-accessible data enables automated processing and integration by the computer (Hogan, 2020). To assess how effectively open data supports the transition to a web of data, Tim Berners-Lee proposed the 5-star deployment scheme for Linked Open Data (LOD). This rating system evaluates the openness and interoperability of data based on a set of key principles. At the most basic level (one-star data), data is merely published on the web, regardless of format. As the data becomes more structured and adheres to semantic web standards, it progresses through higher levels of the scheme. The highest level, five-star linked open data (LOD), represents dataset that is semantically described, structured in standard formats, and interlinked with other datasets. This transition from isolated, file-based datasets to interconnected, structured data allows scattered data to be connected into a global knowledge ecosystem, paving the way for more intelligent data use.
The concept of LOD is intrinsically linked to the Semantic Web, as it relies on semantic web technologies such as RDF triples (Resource Description Framework), SPARQL, and ontologies to structure and interlink datasets across the web. By adhering to the LOD principles, use of URIs, HTTP access, RDF structure, and links to other datasets, LOD facilitates data discoverability, interoperability, and reusability, ultimately allowing for richer, more insightful analyses across multiple domains.
The growing demand for semantically interoperable population census data (LOD) has exposed the limitations of traditional data dissemination formats, prompting the need for more flexible and machine-processable solutions. In Croatia, national statistical agency provides geocoded census data as open data primarily in. xslx format, which imposes significant constraints on automated data processing, and cross-domain analysis. To overcome these limitations, this research aims to provide the basis for publishing the Croatian census as LOD by utilizing the capabilities of OpenRefine, a fully open source data processing tool. By using open source technology, we ensure that each step of the transformation – from data cleaning to RDF triple generation – is transparent, reproducible and adaptable to diverse datasets and research contexts, which is in line with the principles of openness advocated by FOSS4G communities.
To achieve the proposed goal, the methodology includes three main steps: (1) data source identification, (2) semantical description of the census data and (3) data transformation to RDF triples. The census data provided by the Croatian Bureau of Statistics is identified as the primary dataset. This data pertains to the 2021 Census, is aggregated at the administrative spatial unit level, and is provided in .xlsx format. Additionally, corresponding spatial unit geometries are obtained from the State Geodetic Administration, which provides geospatial boundaries in ESRI Shapefile format. Data semantical descriptions reuse existing vocabularies and ontologies to define a structured conceptual model for census data. Specifically, the W3C RDF Data Cube Vocabulary is employed to model the semantic structure of census attributes, while the OGC GeoSPARQL Query Language is integrated to incorporate geospatial components, ensuring that census data is linked to its corresponding spatial regions and geometries. In the final step, .xlsx census data are transformed into RDF triples using OpenRefine. The existing tabular structure is mapped to the RDF Data Cube schema, with spatial units classified as qb:Dimension, population counts as qb:MeasureProperty, and units of measurement as qb:AttributeProperty. Finally, GeoSPARQL classes are utilized to extend spatial units with geospatial properties, such as polygon geometries.
Publishing census data as LOD using Open Refine data manipulation tool demonstrates that existing open technology and conceptual models can easily support the transition to a web of data. However, a potential limitation of the presented approach lies in its realiance on predefined concepts withing the RDF Data Cube schema, rather than extending the ontology to include more detailed domain-specific concepts. Nonetheless, converting Croatia’s census data into LOD represents a significant step toward improved data integration, enhanced accessibility, and the generation of new insights. Future research efforts in this direction may focus on expanding the scope of LOD cloud by integrating housing census data and establishing linkages between population and housing statistics in an LOD framework.</abstract>
<slug>foss4g-europe-2025-3982-open-technologies-supporting-linked-open-data-publishing-croatian-population-census-case-study</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>true</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/JQMHCW/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/JQMHCW/feedback/</feedback_url>
</event>
<event guid="420dc987-6a23-5922-a15a-ef4d6265e242" id="3975">
<room>PA01</room>
<title>Spatial Parameter Analysis of Ground-Mounted Photovoltaic Systems Utilizing Orthophotos and the Segment Anything Model</title>
<subtitle/>
<type>Talk</type>
<date>2025-07-18T14:00:00+02:00</date>
<start>14:00</start>
<duration>00:30</duration>
<abstract>Introduction and Research Objective
Under the Renewable Energy Sources Act (EEG 2023), Germany's installed capacity of solar photovoltaic (PV) systems is projected to increase from 81.7 GW in 2023 to 400 GW by 2040, making them a key pillar of the country's energy transition. This rapid expansion of ground-mounted PV systems necessitates monitoring tools to assess their environmental impact and ensure compliance with regulatory frameworks. One such framework is Section 37 of EEG 2023, which mandates e.g. that solar modules must not cover more than 60% of the total solar park.
However, there is currently a lack of precise spatial data on PV installations across Germany, which poses major challenges for those involved in quantitatively assessing the conflicting goals of nature conservation and energy use. This missing data limits the ability to evaluate compliance with regulations and assess the effectiveness of conservation measures implemented alongside renewable energy development.
The Marktstammdatenregister (MaStR), an open registry managed by the German Federal Network Agency, provides point-based location data for PV systems but lacks essential spatial details, such as 1) proportion of the total area covered by solar modules, 2) distance between module rows and 3) orientation in degrees to which the solar modules are aligned. These spatial parameters are crucial for understanding the ecological and regulatory impacts of PV systems, such as their effects on biodiversity and compliance with ecological guidelines. In this study, we aim to derive information from orthophotos about the listed parameters for all ground-mounted PV systems in Germany. Specifically, we employ the Segment Anything Model (SAM) (Kirillov et-al., 2023), a state-of-the-art zero-shot segmentation model, in combination with digital orthophotos (DOP20) with a ground resolution of 20 cm per pixel. SAM enables precise segmentation of objects or regions in images, allowing us to identify and delineate the components of PV plants with high accuracy.
Data and Methodology
For this study, we utilized an open-access dataset by Manske et al. (2022) available at Zenodo. This dataset contains manually digitized areas of 7,839 ground-mounted PV plants across Germany, serving as a reference to identify and locate corresponding DOP20. The DOP20, featuring red, green, blue (RGB), and near-infrared bands, are made publicly available under Germany’s Second Open Data Act (effective July 2021), offering the necessary spatial resolution for detailed mapping. The temporal resolution is dependent on the federal state and varies between 1 and 3 years.
To prepare the DOP20 images for use with SAM, we cropped the images intersecting with PV plants into 640x640 pixel image chips with an overlap of 340 pixels. This process resulted in the creation of a dataset comprising over 350,000 image chips, which formed a grid for segmentation. The SAM segmentation process was conducted using only the RGB bands of the orthophotos. After generating segmentation masks, we extracted the pixel values for all available bands and derived spectral indices, including the Normalized Difference Vegetation Index, Photovoltaic Index, and Normalized Impervious Surface Index. The pixel values for each mask were aggregated into median values for each segment. Subsequently, we conducted iterative unsupervised clustering using the DBSCAN algorithm. The clustering process comprised two main steps:
1. clustering based on spectral indices and NIR bands to filter out vegetation, shadows, and other non-PV objects as outliers.
2. geometric property clustering, including segment size, rectangularity, orientation, and percent area difference to their oriented bounding box, to remove additional outliers (e.g., irregular shapes overlapping with pathways or transformer stations).
In the final post-processing step, overlapping segments (due to overlapping images) were merged and, where sufficient rectangularity (≥0.8) was achieved, rectangular bounding boxes were generated. Only segments classified as PV module rows were retained for further analysis.
One of the primary challenges in this pipeline was the unsupervised filtering of PV modules, as the DOP20 images were captured under varying seasonal, solar, and viewing angle conditions. Fixed clustering rules were unsuitable due to the diverse spectral properties of PV modules, making unsupervised clustering the only viable approach to classify PV rows.
Results
Preliminary results from this ongoing work demonstrate that it is highly feasible to extract high-quality PV module rows from DOP20 images using SAM. Only about 5% of the results exhibited unsatisfactory segmentation, where module rows and inter-row spaces were not properly separated and grouped into the same segment.
The details of the PV plants analyzed to date are as follows:
• The proportion of the total area covered by solar modules is approximately 65%. However, these installations were constructed before the EEG 2023 regulation came into effect on January 1, 2023, and are thus exempt from the new coverage limit.
• The distance between module rows averaged 3 meters, with considerably larger spacing observed in PV sun-tracking systems.
• The orientation of solar modules predominantly faced to the south (180° azimuth). Approximately 80% of module rows deviated by up to 20° east (azimuth -20°) or 20° west (azimuth +20°).
Since the analysis is still ongoing, additional results will be added in the presentation.
Discussion and Outlook
The vector products generated through our pipeline have significant implications for both policy-making and research. Derived plant details will be made freely available under an open licence, enabling further studies on the environmental impacts of PV systems and supporting decision-making by regulatory bodies.
By applying this segmentation pipeline to newly recorded DOP20 datasets, typically updated every two years across Germany, our approach offers a scalable solution for long-term monitoring of PV system dynamics. This capability is critical given the rapid expansion of PV systems and the potential conflicts between renewable energy development and nature conservation.
Our methodology aligns with the goals of the FOSS4G Europe Academic Track, emphasizing the use of open data, open-source tools, reproducible workflows and therefore ensures full transparency. The complete workflow will be made publicly available under an appropriate open-source license to foster collaboration and innovation within the geospatial and renewable energy communities.</abstract>
<slug>foss4g-europe-2025-3975-spatial-parameter-analysis-of-ground-mounted-photovoltaic-systems-utilizing-orthophotos-and-the-segment-anything-model</slug>
<track>Academic track</track>
<language>en</language>
<recording>
<license/>
<optout>false</optout>
</recording>
<links/>
<attachments/>
<url>https://talks.osgeo.org/foss4g-europe-2025/talk/EU3CB3/</url>
<feedback_url>https://talks.osgeo.org/foss4g-europe-2025/talk/EU3CB3/feedback/</feedback_url>
</event>
</room>
</day>
</schedule>
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