@article{4de62635-4524-40d5-8656-6b384e52f44b,
  abstract     = {{<p>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.</p>}},
  author       = {{Janowicz, Krzysztof and Mai, Gengchen and Huang, Weiming and Zhu, Rui and Lao, Ni and Cai, Ling}},
  issn         = {{1365-8816}},
  keywords     = {{AI alignment; foundation models; GeoAI; spatially explicit machine learning}},
  language     = {{eng}},
  number       = {{9}},
  pages        = {{1849--1865}},
  publisher    = {{Taylor & Francis}},
  series       = {{International Journal of Geographical Information Science}},
  title        = {{GeoFM : how will geo-foundation models reshape spatial data science and GeoAI?}},
  url          = {{http://dx.doi.org/10.1080/13658816.2025.2543038}},
  doi          = {{10.1080/13658816.2025.2543038}},
  volume       = {{39}},
  year         = {{2025}},
}