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In recent years we have seen substantial advances in foundation models forartificial intelligence, including language, vision, and multimodal models.Recent studies have highlighted the potential of using foundation models ingeospatial artificial intelligence, known as GeoAI Foundation Models, forgeographic question answering, remote sensing image understanding, mapgeneration, and location-based services, among others. However, the developmentand application of GeoAI foundation models can pose serious privacy andsecurity risks, which have not been fully discussed or addressed to date. Thispaper introduces the potential privacy and security risks throughout thelifecycle of GeoAI foundation models and proposes a comprehensive blueprint forresearch directions and preventative and control strategies. Through thisvision paper, we hope to draw the attention of researchers and policymakers ingeospatial domains to these privacy and security risks inherent in GeoAIfoundation models and advocate for the development of privacy-preserving andsecure GeoAI foundation models.

Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models