# Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models arXiv: https://arxiv.org/abs/2309.17319 ยท HTML: https://mlsanity.com/paper/2309.17319/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 39 references that are in the map. - First posted on arXiv 29 Sep 2023. ## Builds on (most cited first, 30 of 32) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022) - [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022) - [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023) - [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022) - [Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://mlsanity.com/paper/2305.18462/) (2023) - [Towards Building the Federated GPT: Federated Instruction Tuning](https://mlsanity.com/paper/2305.05644/) (2023) - [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022) - [Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2305.14710/) (2023) - [On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence](https://mlsanity.com/paper/2304.06798/) (2023) - [Adaptive Reward-Poisoning Attacks against Reinforcement Learning](https://mlsanity.com/paper/2003.12613/) (2020) - [BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT](https://mlsanity.com/paper/2304.12298/) (2023) - [Bag of Tricks for Training Data Extraction from Language Models](https://mlsanity.com/paper/2302.04460/) (2023) - [CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations](https://mlsanity.com/paper/2305.01118/) (2023) - [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020) - [Autonomous GIS: the next-generation AI-powered GIS](https://mlsanity.com/paper/2305.06453/) (2023) - [Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models](https://mlsanity.com/paper/2305.11414/) (2023) - [Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models](https://mlsanity.com/paper/2304.10597/) (2023) - [AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology](https://mlsanity.com/paper/2306.10095/) (2023) - [Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models](https://mlsanity.com/paper/2309.04041/) (2023) - [Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Science](https://mlsanity.com/paper/2304.06508/) (2023) - [Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence](https://mlsanity.com/paper/2304.03892/) (2023) ## Built on by (most cited first, 12 of 12) - [A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models](https://mlsanity.com/paper/2502.17516/) (2025) - [Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations](https://mlsanity.com/paper/2505.17136/) (2025) - [A Survey on Safe Multi-Modal Learning System](https://mlsanity.com/paper/2402.05355/) (2024) - [The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective](https://mlsanity.com/paper/2407.08583/) (2024) - [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023) - [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024) - [Whose Truth? Pluralistic Geo-Alignment for (Agentic) AI](https://mlsanity.com/paper/2508.05432/) (2025) - [Pre-trained Transformer Uncovers Meaningful Patterns in Human Mobility Data](https://mlsanity.com/paper/2406.04029/) (2024) - [Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectories](https://mlsanity.com/paper/2409.00335/) (2024) - [Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events](https://mlsanity.com/paper/2411.12880/) (2024) - [Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Models](https://mlsanity.com/paper/2310.18390/) (2023) - [Trust in foundation models and GenAI: A geographic perspective](https://mlsanity.com/paper/2510.17942/) (2025) ## Abstract (arXiv) 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.