Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Agenda Overview |
| Date: Tuesday, 05/May/2026 | |
| 9:00am - 9:30am | Registration |
| 9:30am - 10:30am | Introduction to the conference - welcome and high-level opening Location: Big Hall |
| 10:30am - 10:45am | Coffee break Location: Externat Tent |
| 10:45am - 11:45am | Plenary session - Earth Observation for official statistics Location: Big Hall |
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ID: 153
/ 1.3: 1
Streamlining of reporting requirements across EU policies for reduction of the reporting burden, and integration of Earth Observation (EO) in official statistics 1EUROPEAN COMMISSION, Belgium; 2Arcadia SIT s.r.l., for the Joint Research Centre, European Commission; 3Independent Consultant Aiming to enhance the uptake of Earth Observation (EO) in EU official statistics and identifying ways to reduce/rationalise the reporting burden of EU Member States through increased use of EO, the Knowledge Centre on Earth Observation (KCEO) conducted a comprehensive survey across 12 European Commission Directorates-General (DGs): the ensuing assessment explored opportunities for streamlining EU-policy reporting obligations and further integrate EO in EU statistics. Reporting programmes within some policy areas present significant opportunities for consolidated data production across policies, following the principle: “measure once, report many times”. A more extensive integration of EO information in reporting represents a paradigm shift, leading to substantial reduction of reporting obligation. In relation to EU official statistics, the analysis of survey’s submissions revealed that there is already some integration of EO-derived information in some policy areas (e.g., environmental monitoring, renewable energy). Transition to increasingly EO-based policy reporting will translate as well into more integration of similar products into Member States mandatory reporting to EUROSTAT. Such transition will enable EUROSTAT to access unprecedented volumes of spatially explicit, temporally consistent, authoritative information, delivering multiple strategic benefits: (1) independent validation of survey-based statistics, (2) geographical (gaps-less) harmonisation in data collection across EU, (3) increased frequency of statistical updates, (4) development of indicators addressing emerging policy priorities including Sustainable Development Goal indicators, climate change adaptation, and urban development monitoring. Further integration of EO information in monitoring, reporting and EU-wide statistics will require a few key strategic actions: (1) development of multi-scale EO data indicators serving local to EU-wide policy needs, beginning with priority domains where EO integration is most mature (e.g., land cover), (2) ensuring the semantic interoperability of policy-specific indicator requirements across policy frameworks, (3) investment in validation methodologies ensuring EO-derived statistics meet official statistics quality standards, and (4) capacity building across DGs and Member State agencies, to maximize utilization of enhanced geospatial capabilities. ID: 227
/ 1.3: 2
Earth Observation for Statistics (EO4S) in EUROSTAT 1European Commission DG EUROSTAT, Luxembourg; 2Sword Group The Warsaw Memorandum signed in 2021 by the National Statistical Institutes (NSIs) set out to explore the benefits of Earth Observation (EO) data for producing statistics. Since 2023, EUROSTAT has been carrying out several activities to implement the Memorandum‘s follow-up action plan. A Task Force on Earth observation involving NSIs has been carrying out regular meetings and is now implementing diverse work packages aiming at designing guidelines and streamlining the process of using EO data withing the European Statistics System. EUROSTAT manages grants that cover funding for innovation statistical projects focussing on EO. EUROSTAT’s alignment with DG DEFIS and the use of the Copernicus Data Space Ecosystem (CDSE) is a highlight of cooperation efforts within the European Commission and has enabled a rich source of IT infrastructure for the EO operations. Research in concrete applications and methodologies was carried out in the areas of agricultural, energy, land use and air quality statistics. ID: 278
/ 1.3: 3
Earth Observation Support to Nature Policies European Environment Agency Recent European policy developments place increasing reliance on Earth Observation (EO) for the assessment and monitoring of biodiversity, nature and ecosystems. Legislative initiatives such as the Nature Restoration Regulation (NRR) and Soil Monitoring Law (SML) both specify measurable targets that require consistent, repeatable, and statistically robust indicators. Other policy files also require similar monitoring support. There is therefore a growing expectation that EO-derived products will deliver operationally stable metrics that can support the regulatory process. This presentation aims to provide an overview of how EO is referenced across policy files, how monitoring expectations are being formulated, and what this implies for practices in the EO domain. As environmental legislation evolves, policymakers are placing greater emphasis on spatially explicit and repeatable information. For biodiversity and ecosystems, Earth Observation is increasingly explored as a cost-effective approach to habitat mapping, vegetation condition assessment, structural ecosystem indicators, among others. These applications place significant demands on timeseries consistency, statistical design, uncertainty estimation, traceability, and model validation. As reliance on EO grows, the requirement for high-quality in situ reference data becomes equally important, both for algorithm development and for independent validation. Hybrid monitoring approaches, taking both into account, are therefore central to delivering results that can be used confidently in future integration of Earth Observation products in national reporting and policy evaluation. The main topics of the presentation:
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| 11:45am - 12:15pm | Coffee break Location: Externat Tent |
| 12:15pm - 1:15pm | Plenary session - Agriculture statistics Location: Big Hall |
| 1:15pm - 2:30pm | Lunch break Location: Canteen |
| 2:30pm - 4:00pm | Workshop - User needs, Experiences, Challenges Location: Big Hall |
| 2:30pm - 4:00pm | Workshop - User needs, Experiences, Challenges Location: Magellan |
| 2:30pm - 4:00pm | Workshop - User needs, Experiences, Challenges Location: James Cook |
| 4:00pm - 4:30pm | Coffee break Location: Externat Tent |
| 4:30pm - 5:30pm | Hands-on demos |
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ID: 222
/ 1.2.3: 1
Leveraging the APEx Solutions for EO-based statistics: Execute, Analyse and Visualise VITO, Belgium ID: 286
/ 1.2.3: 2
Build your own EO statistics showcase with the APEx Geospatial Explorer 1Sparkgeo, United Kingdom; 2VITO; 3ESA ID: 259
/ 1.2.3: 3
Synergies between automated EO image analysis and in-situ observations for area estimation Université catholique de Louvain, Belgium ID: 109
/ 1.2.3: 4
Standardised and Scalable EO Workflows using openEO offered by Copernicus Data Space Ecosystem VITO, Belgium |
| 4:30pm - 5:30pm | Thematic sessions - Agriculture I Location: Big Hall |
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ID: 122
/ 1.2.1: 1
Cross-border cropland indicators and field-scale rice system mapping from multi-sensor Earth observation in the Senegal River Valley 1German Aerospace Center, Germany; 2Instute for Geography and Geology, University of Wuerzburg ID: 125
/ 1.2.1: 2
Comparison and Independent Validation of Global High-resolution Remote Sensing Cropland Extent Products 1Digital FAO and Agro-Informatics Division, Food and Agriculture Organization of the United Nations; 2Statistics Division, Food and Agriculture Organization of the United Nations; 3Land and Water Division, Food and Agriculture Organization of the United Nations ID: 196
/ 1.2.1: 3
Implementation of a standardised approach for crop type area estimation and mapping as part of the Coperncisu4GEOGLAM initiative 1GAF AG, Germany; 2VITO, Belgium; 3TerraSphere, The Netherlands; 4VH Consultores, Mozambique; 5GISBOX, Romania; 6Seidor S.A., Spain under contract with the European Commission, JRC, Ispra (VA), Italy ID: 205
/ 1.2.1: 4
Comparing Earth Observation and Traditional Survey Approaches for Estimating Rice Harvested Area: A Case Study from Indonesia BPS Statistics Indonesia, Indonesia ID: 213
/ 1.2.1: 5
Operational Crop Mapping at Scale: How ESA WorldCereal Supports Agricultural Statistics 1VITO, Belgium; 2WUR, Netherlands; 3IIASA, Austria; 4University of Strassbourg, France; 5University of Valencia, Spain; 6GISAT, Czech Republic; 7GEOGLAM Secretariat, Switzerland; 8European Space Agency, Italy ID: 221
/ 1.2.1: 6
Mapping minor and mixed crops in Zambia and Zimbabwe using ESA WorldCereal crop classification system 1International Maize and Wheat Improvement Center (CIMMYT); 2University of Strasbourg, France; 3VITO, Belgium; 4International Institute for Applied Systems Analysis, Austria; 5University of Maryland, College Park, USA; 6European Space Agency ID: 298
/ 1.2.1: 7
Importance of In Situ data for EO integration in agricultural statistics: requirements and opportunities 1UCLouvain, Belgium; 2FAO ID: 252
/ 1.2.1: 8
The Copernicus Agricultural Mapping Service to support Food Security: Copernicus4GEOGLAM 1European Commission, Joint Research Centre (JRC), Ispra (VA), Italy; 2Seidor Consulting, Barcelona, Spain | Under contract with the European Commission, JRC, Ispra (VA), Italy; 3GAF AG, München, Germany; 4VITO Remote Sensing, Mol, Belgium; 5Terrasphere, Amsterdam, The Netherlands |
| 4:30pm - 5:30pm | Thematic sessions - People & Urban areas Location: Magellan |
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ID: 136
/ 1.2.2: 1
Harnessing EO and census data for subnational risk analyses of environmental hazards Organisation for Economic Co-operation and Development (OECD), France ID: 151
/ 1.2.2: 2
Mapping Urban Realities: Integrating Citizen Science and Earth Observation for the UMF 1International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria; 2Citizen Science Global Partnership (CSGP), Laxenbug, Austria; 3The Bartlett Centre for Advanced Spatial Analysis (CASA), University College London, London, United Kingdom; 4UN-Habitat (United Nations Human Settlements Programme), Nairobi, Kenya ID: 198
/ 1.2.2: 3
Spatial Indicators of Soil Sealing for Environmental Monitoring in the Mediterranean: The Ulysses Med Land Approach Planetek Italia ID: 202
/ 1.2.2: 4
Yearly Urban Tree Canopy and Urban Green Space Coverage Indicators for Germany from Sentinel-2: An Operational Workflow for Deriving Indicators for the EU Nature Restoration Regulation Luftbild Umwelt Planung, Germany ID: 233
/ 1.2.2: 5
Integrating Earth Observation and Statistical Data through Location-Based Frameworks 1U.S. Census Bureau; 2European Commission Joint Research Centre; 3United Nations ID: 249
/ 1.2.2: 6
Co-development of small area population estimates with governments to fill demographic data gaps WorldPop, University of Southampton, United Kingdom ID: 272
/ 1.2.2: 7
A multiscale demand analysis applied to urban cultural ecosystem services: an application in Hannover, Braunschweig (Germany); Milan, Naples (Italy) Leibniz University Hannover, Italy |
| 5:30pm - 6:00pm | Workshop reporting to plenary Location: Big Hall |
| 6:00pm - 7:30pm | Ice breaker with Poster session Location: Externat Tent |
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ID: 237
/ 1.6: 1
Earth Observation for Statistics (EO4S) in EUROSTAT 1European Commission DG EUROSTAT, Luxembourg; 2Sword Group ID: 127
/ 1.6: 2
Mapping Urban Trees from Space to support EU Green policies and SDGs CLS, France ID: 112
/ 1.6: 3
Leveraging citizen science, Earth Observation, and AI for plastic litter to inform official statistics, SDG reporting, and policy development 1International Institute for Applied Systems Analysis (IIASA); 2SciDrones; 3Ghana Statistical Service (GSS); 4Sustainable Development Solutions Network (SDSN) ID: 195
/ 1.6: 4
Developing Policy-Relevant Mangrove Statistics from EO: Results from the GDA Marine Activity in Cambodia, Ecuador and Guinea-Bissau Planetek Italia ID: 181
/ 1.6: 5
Reducing Cross-Policy Reporting Burden Through Earth Observation Integration: an example of peatlands and carbon monitoring EC-JRC, Italy ID: 105
/ 1.6: 6
Scaling Biodiversity Estimation from Sparse Data using Aerial Imagery and Semi-Supervised Learning Statistics Netherlands ID: 269
/ 1.6: 7
Tackling the challenge of monitoring SDG indicators for fisheries in SIDS 1Indra Space, Portugal; 2AIR Centre, Portugal ID: 157
/ 1.6: 8
Enhancing the Finnish construction project start statistics utilizing EO data 1Finnish Environment Institute; 2Statistics Finland ID: 248
/ 1.6: 9
EO-assisted estimation enhances the precision of National Forest Inventory indicators, also in a data-poor context 1Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, the Netherlands; 2Sustainable Forest Ecosystems, Wageningen Environmental Research, the Netherlands; 3Forest Ecology and Forest Management Group, Wageningen University & Research, the Netherlands ID: 281
/ 1.6: 10
From Aerial Imagery to Official Statistics: Integrating Registry Data in Operational Deep Learning for Fine-Grained Built-Up Area Mapping in the Netherlands 1Statistics Netherlands (CBS); 2University of Twente ID: 126
/ 1.6: 12
Mapping Urban Trees from Space to support EU Green policies and SDGs CLS, France ID: 277
/ 1.6: 13
Scalable ecosystem indicators on the Baltic GTIF Dashboard 1EOX IT Services GmbH, Austria; 2National Paying Agency Luthiania (NPA) ID: 107
/ 1.6: 14
Building Data from High-Resolution Images Statistics Portugal, Portugal ID: 285
/ 1.6: 15
Mapping Air Pollution Inequality Using Sentinel-5P: Integrating EO and Socio-Economic Data to Support Policy Action Statistics Netherlands, Netherlands, The ID: 242
/ 1.6: 16
Operational reporting of SDG 14.1.1 indicator in Portugal and Cape Verde based on CMEMS data Indra Space, Portugal ID: 208
/ 1.6: 17
terrAIntel: Enabling Thematic Earth Observation Data Exploitation through Natural Language Interfaces and Cloud-Native Workflows 1GeoVille, Austria; 2cortecs, Austria ID: 206
/ 1.6: 18
A predictive model of GDP composition by sector NILU, Norway ID: 312
/ 1.6: 19
High-resolution global land cover maps for national-scale area change estimation and reporting: case study for Uganda 1GFZ Helmholtz Centre for Geosciences, Germany; 2Wageningen University & Research ID: 123
/ 1.6: 20
Development of past and present annual winter wheat yield statistics at the level of administrative districts (“raions”) in South European Russia based on statistical modelling and large scale EO data 1FSBIS Federal Research Centre The Southern Scientific Centre of The Russian Academy of Sciences, Russia; 2Tsnghua University ID: 182
/ 1.6: 21
The Tropical Forest Forever Facility: support tools by the Joint Research Centre Joint Research Centre, Italy ID: 216
/ 1.6: 23
A Standards-Driven Maturity Framework for Ensuring Data Credibility and Scientific Validity for Regulatory Environmental Evidence 1EARSC; 2Institut de Ciències del Mar (ICM-CSIC) ID: 197
/ 1.6: 24
An EO and Economic Data Framework for Estimating the Magnitude and Spatial Distribution of Informal Trade (Bazaar) in Central Asia Planetek Italia ID: 130
/ 1.6: 25
EO-based Grassland Production Index for estimating drought related yield losses: development in mountain environment and current challenges 1Eurac Research, Institute of Earth Observation, Bolzano, Italy; 2Eurac Research, terraxcube, Bolzano, Italy; 3Eurac Research, Center for Climate Change and Transformation, Bolzano, Italy ID: 239
/ 1.6: 26
The Satellite-Based Crisis and Spatial Information Service (SKD) at BKG (Germany): A National EO Service for supporting evidence-based decision-making and policy implementation Federal Agency for Cartography and Geodesy, Germany ID: 293
/ 1.6: 27
From EO Outputs to Policy Decisions: Applying an Impact Framework to Official Statistics Reporting Green Orbit Space Communications and PR, United Kingdom ID: 176
/ 1.6: 28
An Universal and Index-Agnostic Bitemporal Indicator for Unsupervised Environmental Change Detection from Multispectral Satellite Data 1Università Degli Studi di Padova, Italy; 2Engineering Ingegneria Informatica S.p.A ID: 245
/ 1.6: 29
Integrating Pollutant registers for the climate change risk evaluation of industrial companies in Australia, Europe and North America JRC, Italy ID: 265
/ 1.6: 30
Monitoring of SDG 6 indicators in Portugal and Denmark with EO-based algorithms 1DHI, Denmark; 2Indra Space, Portugal; 3AIR Centre, Portugal ID: 241
/ 1.6: 31
Not All Fires Are Equal: Toward a Fire‑Sensitive Land Degradation Index for Africa Climate Impact Partners, Kenya ID: 156
/ 1.6: 32
SDG 15.4.2 Mountain Green Cover-indicator for Finland Finnish Environment Institute, Finland ID: 166
/ 1.6: 33
Training Sample Migration for Temporal Cropland Mapping in Central Asia Food and Agriculture Organization of the United Nations, Italy ID: 190
/ 1.6: 34
Prediction of grassland yield in Austria: A machine learning approach based on satellite, weather, and extensive in situ data 1Institute of Plant Production and Cultural Landscape, Agricultural Research and Education Centre Raumberg-Gumpenstein, Raumberg 38, Irdning‑Donnersbachtal 8952, Austria; 2BOKU University, Austria ID: 304
/ 1.6: 35
SITS-ORDER: Discriminative Error Retrieval for Robust Crop Classification in the US and Brazil 1Brazilian Institute of Geography and Statistics, Brazil; 2Federal University of Viçosa, Brazil; 3University of Sheffield, UK ID: 106
/ 1.6: 36
Assessing Urban Expansion using Copernicus Data Space Ecosystem Data & APIs Sinergise Solutions GmbH, Austria ID: 301
/ 1.6: 37
Fusing LEO and GEO observations for agricultural monitoring 1Φ-lab, European Space Agency (ESA), ESRIN, Via Galileo Galilei, Frascati, Italy; 2Co2 Angels, Cluj-Napoca, Romani ID: 214
/ 1.6: 38
The Use of Satellite Technologies in Mapping Flood Extent and Analysis of Its Impact on the Availability of Ambulances in Flood Areas 1AGH University, Faculty of Space Technologies; 2AGH University, Faculty of Geology, Geophysics and Environmental Protection ID: 175
/ 1.6: 39
The tolerance of spatial statistics for methodological or conceptual ambiguities– exemplified by the degree of urbanization in Germany 1European Space Imaging, Germany; 2German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 Oberpfaffenhofen, Germany; 3Institute for Geography and Geology, Julius-Maximilians-Universitat ¨ Würzburg, 97074 Würzburg, Germany; 4Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR), 53179 Bonn, Germany; 5Federal Institute for Population Research (BIB), 65185 Wiesbaden, Germany; 6German Aerospace Center (DLR), Space Agency, Earth Observation, 53227 Bonn, Germany ID: 307
/ 1.6: 40
Biodiversity Carbon Farming Index (BCFI) - EO-driven Monitoring, Reporting, and Verification (MRV) for Supporting Policy and GHG Inventories 1EOX IT Services, Austria; 2PRO-NATURE Nature Conservation, Austria ID: 193
/ 1.6: 41
Earth Observation and irrigation water accounting from the field to the regional scale: operational support to sustainable management of irrigation water resources. 1University of Naples Federico II, Italy; 2Ariespace srl, Spin off company University of Naples Federico II ID: 184
/ 1.6: 42
Improving forest monitoring and management with fine-scale maps of forest parameters at the EU and global scale 1Flemish Institute for Technological Research; 2International Institute for Applied Systems Analysis; 3Technical University of Munich; 4European Forest Institute; 5Johann Heinrich von Thünen-Institut; 6Agency for Nature and Forests; 7Wageningen Environmental Research; 8Stichting Probos ID: 116
/ 1.6: 43
Mapping rice data to support irrigation performance assessment in the Chokwe irrigation scheme, Mozambique 1Food and Agriculture Organization of the United Nations, 00153 Rome, Italy; 2School of Information Management and Data Science, NOVA University of Lisbon, 1070-312 Lisbon; 3Faculdade de Ciências Agronómicas, Universidade Católica de Moçambique (UCM FCA), Cuamba 3305, Niassa, Mozambique ID: 137
/ 1.6: 44
Automated Deep Learning for Large-Scale Forest Attribute Estimation and Tree Species Mapping from Multi-Source Remote Sensing Data Universitatea Transilvania din Brasov, Romania ID: 204
/ 1.6: 45
Democratising Deforestation Intelligence for Sovereign Finance: A Replicable EO Framework for Sustainability-Linked Bonds in Uganda 1Assimila, United Kingdom; 2University of Oxford, United Kingdom ID: 173
/ 1.6: 46
Reimagining land cover mapping: EO as a catalyst for innovation in Northern Ireland Ordnance Survey of Northern Ireland (OSNI), United Kingdom ID: 260
/ 1.6: 47
Assessing the Potential of Satellite Data to Improve Agricultural Statistics in Spain 1UCLouvain, Belgium; 2Ministry of Agriculture, Fisheries and Food, Spain ID: 212
/ 1.6: 48
AgriGuard: A Regional EO-Based Platform for Agriculture and Hazard Monitoring in Support of Policy and Early-Warning Applications Food and Agriculture Organization of the United Nations (FAO), Italy ID: 315
/ 1.6: 49
From Spectral Signals to Harmonized Statistics: Upscaling Sentinel-2 Yield Stability for Regional Reporting Institute of Landscape Ecology, Slovak Academy of Sciences, Slovak Republic ID: 306
/ 1.6: 50
Open-Pit Mining Detection & Monitoring AGH University of Krakow, Poland ID: 240
/ 1.6: 51
The in situ data bottleneck in Earth observation for agriculture: challenges, barriers, and a path forward 1European Space Agency (ESA), Frascati, Italy; 2PErSEUs, University of Lorraine, Metz, France; 3Psychological Sciences Research Institute, UCLouvain, Louvain-la-Neuve, Belgium ID: 143
/ 1.6: 52
A physics-informed hybrid deep learning model for spatio-temporal rice disease prediction integrating multi-source data 1Hangzhou Dianzi University, China, People's Republic of; 2Zhejiang University of Water Resources and Electric Power, China, People's Republic of; 3Food and Agriculture Organization of the United Nations, Italy ID: 243
/ 1.6: 53
EO-based detection of disturbance in grasslands and small landscape elements to support environmental policy enforcement 1VITO, Unit Environmental Intelligence, Group Remote Sensing; 2ANB (Agency of Nature and Forest), Group Nature inspection; 3DV (Agency Digital Flanders), Group Earth Observation Data Science ID: 254
/ 1.6: 54
From Subregional Statistics to Farms: How Can AI Learn Crop Yield at This Scale? Joint Research Centre, Italy ID: 210
/ 1.6: 55
AI-Powered Web-GIS Platform for EO-Based Transport Infrastructure Monitoring and Risk Management 1TITAN4 S.r.l., Via dell’Arte 19, 00144 Rome, Italy; 2Department of Earth Science, University of Roma Tre, Via Ostiense, 133, 00154 Rome, Italy; 3Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Via Vitaliano Brancati, 48, 00144 Rome, Italy ID: 302
/ 1.6: 56
A Comprehensive Framework for Scalable and Cost-Effective Crop Monitoring: Leveraging Parcel Segmentation and Satellite Image Time-Series. 1Brazilian Institute of Geography and Statistics, Brazil; 2Federal University of Viçosa, Brazil; 3University of Sheffield, UK ID: 228
/ 1.6: 57
Assessing Post-Fire Land Cover Evolution in Pisani Mountains: A Random Forest Approach with Bootstrapped NDVI Trend Analysis using Sentinel-2. Italian Institute fo Environmental Protection and Research, Italy ID: 164
/ 1.6: 58
Evaluating Deep Learning based Building Damage Assessment Methods in earthquake-affected, densely built-up urban areas: The case of Kahramanmaraş OECD, Paris ID: 255
/ 1.6: 59
Monitoring Inland Water Quality in Poland Using Python and Sentinel-2 Satellite Imagery AGH University of Krakow, Poland ID: 148
/ 1.6: 60
Wildforest Urban Interface and Earth Observation role on policy implementation Institute Cartographic and Geological of Catalonia, Spain ID: 246
/ 1.6: 62
From public geodata to a multi-dimensional 3d cadastre - a legal-environmental Digital City Twin Concept for Krakow University of Agriculture in Krakow, Poland ID: 179
/ 1.6: 63
GeoBioRemediation: EO for EU Soil Monitoring Compliance Antarix Space srl, Italy ID: 138
/ 1.6: 64
Leveraging Remote Sensing for Enhanced Irrigation Performance Assessments in Data-Limited and Water-Scarce Regions Northern Jordan Valley as a Case Study IWMI, Jordan, Hashemite Kingdom of ID: 144
/ 1.6: 65
Operational Earth Observation for Wildfire Damage Assessment in Olive Groves: An Integrated Court–Agency Case Study from the Mediterranean Region Ministry of Agriculture and Forestry, Aliağa District Directorate, Turkiye ID: 219
/ 1.6: 66
A Pan-European Dataset of Forest Structural Diversity Indicators Trinity College Dublin, Ireland ID: 262
/ 1.6: 67
EO and Spatial Modeling for Urban Climate Risk Assessment in Eight African Cities 1FAO; 2Sapienza Università di Roma; 3S[&]T Italy ID: 297
/ 1.6: 68
A Deep Learning Framework for Land Use Land Cover Change Forecasting in the Brazilian Amazon 1Φ-lab ESA/ESRIN, Italy; 2IUSS Pavia ID: 295
/ 1.6: 69
A Sample-Based, Multi-Sensor Assessment of Land-Use and Land-Cover Change in Cameroon Using Collect Earth Online 1US Forest Service International Program and Trade; 2Coalition for Rainforest; 3Spatial Informatics Group; 4Observatoire national des changements climatiques; 5Ministry of the Environment, Protection of Nature and Sustainable Development ID: 133
/ 1.6: 70
Can remote sensing support biodiversity certification ? Université catholique de Louvain, Belgium ID: 169
/ 1.6: 71
Enhancing Earth Observation to Track Progress Towards the Global Goal on Adaptation 1ESA, United Kingdom; 2Australian Centre for Human Evolution, Griffith University, Brisbane, QLD, Australia; 3National Aeronautics and Space Administration, Washington, D.C., USA; 4various ID: 120
/ 1.6: 72
Enhancing Macroeconomic Statistics with Sentinel-1: Monitoring Automotive Production in Germany for Timely Economic Indicators 1German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaffenhofen, Germany; 2European Space Agency (ESA), Φ-lab, Earth Observation Climate Action, Sustainability and Science Department (EOP-S), Frascati, Italy ID: 310
/ 1.6: 73
Natural Capital Solutions Platform: Scaling EO-Driven Ecosystem Metrics 1EOX IT Services, Austria; 2PRO-NATURE Nature Conservation NGO, Austria ID: 132
/ 1.6: 74
Monitoring Forest Condition and Disturbance with Sentinel-1 SAR: Indicators for Environmental Reporting 1Department of Geoinformatics—Z_GIS, University of Salzburg, 5020, Salzburg, Austria; 2Department of Applied Geoinformatics and Cartography, Charles University, Albertov 6, 128 43, Prague 2, Czech Republic ID: 288
/ 1.6: 75
Optimizing Crowdsourced Training Samples for Large-Scale Crop Mapping 1The Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China; 2Wageningen Environmental Research, Wageningen University and Research, Wageningen, The Netherlands ID: 162
/ 1.6: 76
Integrating Earth Observation and Survey Data for Bias-Corrected Crop Area Estimation: An Operational Framework Using Sentinel and LUCAS Data TERMA, EUMETSAT, Germany ID: 318
/ 1.6: 77
An ensemble-based approach for continuous monitoring and attribution of vegetation loss agents at regional to national scale using Landsat imagery Aristotle University of Thessaloniki, Greece ID: 226
/ 1.6: 78
Hydro-Climatic Drivers of SAR Backscatter in Vineyards to Support Agricultural Statistics 1Department of Electrical, Computer, Biomedical Engineering, University of Pavia, Pavia, Italy; 2Microwave Remote Sensing Lab (MRSLab), Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai, India; 3Department of Engineering, University of Naples Parthenope, Naples, Italy; 4Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy ID: 292
/ 1.6: 79
When does very high resolution matter? A stratified evaluation of cocoa maps across canopy closure and landscape fragmentation in Côte d’Ivoire 1ITC (University of Twente); 2Joint Research Centre, Italy ID: 296
/ 1.6: 80
Assessing Long‑Term PM2.5 Variability and Public Health Impacts in a Tropical Coastal Region of India 1School of Environmental Studies, Cochin University of Science and Technology, Kochi 682022, Kerala, India; 2CORAL, Indian Institute of Technology, Kharagpur, West Bengal, India; 3Nansen Environmental Research Centre (India), KUFOS Amenity Centre, Kochi - 682506, India. ID: 183
/ 1.6: 81
From Pixels to Policy: Translating Earth Observation into Research-Ready Evidence 1University of Liverpool; 2Harvard University ID: 290
/ 1.6: 82
High-resolution global land cover maps for national-scale area change estimation and reporting: case study for Uganda 1GFZ Helmholtz Centre for Geosciences, Germany; 2Wageningen University & Research ID: 135
/ 1.6: 83
How Earth Observation facilitates the extensive monitoring of woody landscape features and their ecosystem functions 1German Aerospace Center (DLR), Germany; 2Julius-Maximilians-Universität Würzburg, Germany; 3Bayerisches Landesamt für Umwelt (LfU), Germany ID: 316
/ 1.6: 84
Large-scale detection of land-use transitions using multi-temporal satellite data and deep learning Wageningen University and Research, Netherlands, The ID: 211
/ 1.6: 85
EOAgriTwin: A Digital Twin for Agriculture under Multiple Stressors 1Leibniz Centre for Agricultural Landscape Research (ZALF), Germany; 2Remote Sensing Solutions GmbH, Munich, Germany; 3Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy; 4Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany; 5International Centre of Insect Physiology and Ecology, Nairobi, Kenya; 6Earth & Life Institute, UCLouvain, Louvain-La-Neuve, Belgium; 7Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany; 8Global Change Research Institute CAS, Brno, Czech Republic ID: 253
/ 1.6: 86
Integrating Multi-Sensor Earth Observation Data for Coastal Change Indicators and Sea-Level Rise Scenarios: A Case Study from Northern Egypt 1Science, Applications & Climate Department, European Space Agency (ESA-ESRIN), Frascati, Italy; 2African Research Fellow; 3Geology department, Faculty of Science, Port Said University, 42522 Port Said, Egypt; 4Environmental Sciences Department, Faculty of Science, Port Said University, Port Said 42522, Egypt ID: 160
/ 1.6: 87
Geospatial Tools for Green Finance: Supporting Sustainable Project Selection and Impact Measurements Space4Good, Netherlands, The ID: 314
/ 1.6: 88
Remote Sensing of Urban Ecosystem Value: From GDP to Gross Ecosystem Product Using Nighttime Light Data Sapienza University, Rome, Italy, Italy ID: 303
/ 1.6: 89
Urban Area Mapping and Assessment Using Earth Observation and AI: Methods and a Case Study from Arequipa, Peru 1CloudFerro S.A., Poland; 2University of Warsaw, Poland ID: 244
/ 1.6: 90
Flood risk and security prices JRC, Italy ID: 324
/ 1.6: 91
Enabling Agentic capabilities in Earth Observation using EVE – applications in the EO Dashboard and drought monitoring 1European Space Agency, Φ-Lab, Frascati, Italy; 2North Carolina State University; 3European Space Agency, Frascati, Italy |
