Abstract
The emerging field of geo-foundation models (GeoFM) has the potential to reshape GeoAI and spatial data science research, education, and practice. In this work, we motivate and define the term and put it into its historic context within GeoAI and spatial data science more broadly. Next, we review core datasets, models, and benchmarks. Based on this overview of the state-of-the-art, we introduce key research challenges for future GeoFM research, such as GeoAI scaling laws, geo-alignment of AI, truly multimodal GeoFM, and so on. Finally, we discuss potential risks of GeoFM research and outline the road ahead with a specific focus on the increasing role of international large-scale collaborations and the future of GeoAI and spatial data science education.
| Original language | English |
|---|---|
| Pages (from-to) | 1849-1865 |
| Number of pages | 17 |
| Journal | International Journal of Geographical Information Science |
| Volume | 39 |
| Issue number | 9 |
| Early online date | 9 Aug 2025 |
| DOIs | |
| Publication status | Published - 1 Sept 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- AI alignment
- foundation models
- GeoAI
- spatially explicit machine learning
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