Faculty of Mathematics and Natural Sciences - Applied Geoinformation Science

Welcome to the Geoinformation Science Lab

View the results of our internal GIS Day 2022 contest here!

Our Vision 

The focus of the Geoinformation Science Lab lies on the development and application of spatio-temporal techniques to study the human-environment system. In our research projects we address spatio-temporal modeling of regional land use change and the human-environment interface in urban areas.


Research Activities

I Spatio-temporal modeling of land use and land cover change

II Exploring spatial patterns and processes in the human-environment system

We study land change processes on a regional scale using multitemporal remote-sensing-derived land cover data and an integrated dataset of socio-economic as well as environmental data. The overarching aim of our projects is to test and develop diverse, state-of-the-art techniques of spatio-temporal modeling in land system science including cellular automata, Bayesian Belief networks, agent-based modeling, machine learning and advanced regression analysis.

Urban areas represent the living environment of more than half of the world’s population and are characterized by a heterogeneous set of socio-demographic, economical, and environmental factors. We hence study urban areas as human-environment systems following the concepts of vulnerability and risk, environmental justice, spatial epidemiology and ecosystem services provision. In order to gain new insights on spatial patterns and underlying processes in urban areas we investigate spatial data analysis techniques in the fields of data integration and data mining, geostatistics, remote-sensing, and time-series analysis.

Working environment

We provide a working environment in which everybody contributes an important role to the mission of the lab. We strive for valuing social, cultural and individual diversity in an interdisciplinary and international team. In our work we aim for transparent and well-documented workflows using open source software besides well-established proprietary products whenever possible. Furthermore, we strive to comply with FAIR data principles as well as data privacy as far as possible.



We teach several classes on Bachelor and Master level on fundamental and specialist knowledge in both concepts and hand’s on knowledge of applied GIScience. We transfer methods and research topics knowledge into lectures, exercises, seminars, and internships and offer close linkages between research and teaching. We give our students direct insight into the exciting field of applied GIScience and enable them to join us at an early stage as student collaborators or for pursuing final theses. Our students are well prepared for using GIScience in their future professional career.