Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models
It highlights critical privacy and security concerns for researchers and policymakers in geospatial domains, advocating for safer models, but it is incremental as it focuses on raising awareness rather than introducing new methods.
This paper identifies privacy and security risks in GeoAI foundation models used for tasks like geographic question answering and remote sensing, and proposes a research blueprint and strategies to address these issues.
In recent years we have seen substantial advances in foundation models for artificial intelligence, including language, vision, and multimodal models. Recent studies have highlighted the potential of using foundation models in geospatial artificial intelligence, known as GeoAI Foundation Models, for geographic question answering, remote sensing image understanding, map generation, and location-based services, among others. However, the development and application of GeoAI foundation models can pose serious privacy and security risks, which have not been fully discussed or addressed to date. This paper introduces the potential privacy and security risks throughout the lifecycle of GeoAI foundation models and proposes a comprehensive blueprint for research directions and preventative and control strategies. Through this vision paper, we hope to draw the attention of researchers and policymakers in geospatial domains to these privacy and security risks inherent in GeoAI foundation models and advocate for the development of privacy-preserving and secure GeoAI foundation models.