Towards the next generation of Geospatial Artificial Intelligence

Mai, Gengchen [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000278187309); Xie, Yiqun [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000264391333); Jia, Xiaowei [Univ. of Pittsburgh, PA (United States)] (ORCID:0000000185445233); Lao, Ni [Google LLC, Mountain View, CA (United States)] (ORCID:0000000240347784); Rao, Jinmeng [Google LLC, Mountain View, CA (United States)] (ORCID:0000000323705129); Zhu, Qing [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:000000032441944X); Liu, Zeping [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000328980023); Chiang, Yao-Yi [Univ. of Minnesota, Minneapolis, MN (United States)] (ORCID:0000000289230130); Jiao, Junfeng [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000272728805)

doi:10.1016/j.jag.2025.104368

DOE OSTI · 2566093

Towards the next generation of Geospatial Artificial Intelligence

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Abstract

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote sensing, urban computing, Earth system science, cartography, and geospatial semantics. Finally, we highlight several unique future research directions of GeoAI which are classified into two groups: GeoAI method development challenges and GeoAI Ethics challenges. Topics include heterogeneity-aware GeoAI, knowledge-guided GeoAI, spatial representation learning, geo-foundation models, fairness-aware GeoAI, privacy-aware GeoAI, as well as interpretable and explainable GeoAI. We hope our review of GeoAI’s past, present, and future is comprehensive and can enlighten the next generation of GeoAI research.

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58 GEOSCIENCES Geospatial Artificial Intelligence fairness-aware GeoAI geo-foundation models heterogeneity-aware GeoAI interpretable and explainable GeoAI knowledge-guided GeoAI privacy-aware GeoAI spatial representation learning

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Mai, Gengchen [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000278187309), Xie, Yiqun [Univ. of Maryland, College Park, MD (United States)] (ORCID:0000000264391333), Jia, Xiaowei [Univ. of Pittsburgh, PA (United States)] (ORCID:0000000185445233), Lao, Ni [Google LLC, Mountain View, CA (United States)] (ORCID:0000000240347784), Rao, Jinmeng [Google LLC, Mountain View, CA (United States)] (ORCID:0000000323705129), Zhu, Qing [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)] (ORCID:000000032441944X), Liu, Zeping [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000328980023), Chiang, Yao-Yi [Univ. of Minnesota, Minneapolis, MN (United States)] (ORCID:0000000289230130), Jiao, Junfeng [Univ. of Texas, Austin, TX (United States)] (ORCID:0000000272728805). 2025-01-20. Towards the next generation of Geospatial Artificial Intelligence. https://doi.org/10.1016/j.jag.2025.104368

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