Places can be described based on various different characteristics, such as whether a given place is forested or barren, its height above sea level, what animals are found there and whether there are buildings, roads or parks. Such information is generally stored in classic geodatabases of maps, satellite images, elevation models, etc. This practice tends to create problems however, because “the data exist in differing formats, resolutions and grid sizes, so it takes major effort to utilize them in combination,” as Junior Professor Marc Rußwurm of the University of Bonn Institute for Food and Resource Economics explains. “Harmonizing such geodata to make it usable in modern AI methods is a lot of work”. This can mean combining animal photos from camera traps with vegetation, altitude, climate and human infrastructure data in order to predict whether certain species will find suitable habitats there.
AI is learning to better “understand” places
The new Emmy Noether Group will investigate how geodata can be represented within the parameters of artificial neural networks. Rußwurm and his team are developing AI methods to synthesize such different data types to derive a uniform geospatial representation. The goal is for AI to achieve a better “understanding” of places than it currently has. “People often rely on pictures and maps to get a sense of what a place is like without actually being there themselves—whether warm or cold, green or intensively developed, crowded or deserted. Our work is aimed at enabling AI to use this kind of data to know more about places in similar fashion.”
The new AI methods developed have diverse application potential, such as allowing more precise urban quality-of-life analysis by correlating location characteristics with resident satisfaction data or real estate prices. “By drawing on different kinds of geoinformation, AI could also project what coastlines and beaches are subject to elevated plastic waste levels,” Rußwurm observes. Global mapping of vegetation and settled areas could also be made more precise, as AI would be more aware of regional differences.
Transdisciplinary AI research
The breadth of possible applications indicates the transdisciplinary nature of this work, which is why the University of Bonn is an ideal research center for it. Rußwurm, who moved here from the Netherlands at the start of the year, will be collaborating with colleagues from different disciplines within the framework of the University of Bonn’s Transdisciplinary Research Areas (TRAs) Modelling and Sustainable Futures. The collaborative purpose is to study how AI methods can be employed to more effectively evaluate local biodiversity, gauge microplastic soil content over large areas, represent global Earth gravity in AI models and reveal how biodiversity and other environmental changes correlate with political and societal decision-making processes. “What makes the University of Bonn so attractive to me is how fundamental research and applied research really go hand in hand here.”
Bio
Marc Rußwurm has been junior research group leader at the University of Bonn’s Machine Learning in Earth Observation (MEO) Lab since February 2026. His previous position was Assistant Professor of Machine Learning and Remote Sensing at Wageningen University, and he has experience in geodesy and geoinformation. Starting in September 2026 Rußwurm will be head of the Emmy Noether Group “Earth Embeddings: Learning Concept Maps in Neural Nets,” backed by roughly 800,000 euros in initial funding from the German Research Foundation (DFG) for a three-year period. Around 600,000 euros in follow-on funding may be granted for a three-year extension after passing an interim evaluation. The funding is provided as part of the DFG's involvement in the Global Minds Initiative Germany by the Federal Ministry of Research, Technology and Space.