Work

GeoFM: how will geo-foundation models reshape spatial data science and GeoAI?

Year: 2025

Type: article

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.

Source: International Journal of Geographical Information Systems

DOI: 10.1080/13658816.2025.2543038

Authors: Krzysztof Janowicz, Gengchen Mai, Weiming Huang, Rui Zhu, Ni Lao, Ling Cai

Institutions: University of Vienna, The University of Texas at Austin, University of Leeds, Nanyang Technological University, Lund University, University of Bristol, Google (United States), ByteDance (China)

Language: English

Topic: Geographic Information Systems Studies

Subfield: Geography, Planning and Development

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4413237909

FWCI: 194.6

Cites: 97

Cited by: 44

Related to: 10