Research
Research Labs
It combines satellite-based Earth observation with geoinformatics to support humanitarian missions—its industry partner is Médecins Sans Frontières (Doctors Without Borders). The research group advances information extraction from satellite imagery using deep learning and object-based image analysis, integrating results with OpenStreetMap and statistical data, and ultimately communicating findings to operational decision-makers. Typical applications include population estimation in conflict areas, camp planning, and disaster monitoring.
Works with Big Earth Observation data—especially from open archives such as Copernicus—and develops methods for automated semantic image understanding in large image databases. Key focus areas include combined spatiotemporal analysis of long time series, as well as handling uncertainty and data quality in large-scale archives.
Conducts fundamental and applied research in spatial AI, spatial knowledge graphs, and semantics/ontologies in geography. The work also includes agent-based modeling and numerical spatial optimization. Application areas include, among others, the energy transition and CO₂ footprint, supply-chain analysis, and tourism.
Helps organizations use geospatial technologies to meet European sustainability regulations such as the CSRD, ESRS, and the EU Deforestation Regulation. Topics include satellite-based monitoring of biodiversity, climate risk, and deforestation, as well as the creation of automated GIS workflows for sustainability reporting.
Treats landscapes as complex, constantly changing systems and uses a transdisciplinary approach to identify the drivers of landscape change. Methodologically, the group combines automated “geosynthesis” from web services with sensor networks and real-time processing.
Researches at the intersection of geoinformatics and mobility research. Core topics include data modeling and network analysis of transport networks, as well as modeling the interaction between moving objects and their environment. The group places a strong emphasis on transferring methodological innovations into real-world application through collaborations.
Focuses on openness and transparency in spatial research—from reproducible, well-documented analysis workflows and guidelines for reproducibility in academic publications and conferences to open educational resources. Another strand is the assessment of competencies and skills in the Earth Observation and geoinformatics sector.
Identifies spatial patterns of risk, hazard, and vulnerability using geoanalysis and remote sensing. Topics include mass movements, flooding, landscape dynamics, and climate adaptation, supported methodologically by automated feature extraction, InSAR, time-series analysis, and machine learning.
Uses simulation models as “virtual laboratories” to investigate how spatial patterns emerge from the behavior of individuals and their local interactions. The focus is on agent-based spatial modeling; comparing simulation results with real observations is used to test and refine understanding of the system.
