mlsanityData to 14 Oct 2026

Building Privacy-Preserving and Secure Geospatial Artificial Intelligence Foundation Models

arXiv 2309.17319 ↗First posted 29 Sep 2023cs.AIcited by 12

Not on the map. Why?

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.

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

32 of 39 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2023Segment Anythingcited by 8,234
  5. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  8. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  9. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  10. 2020Extracting Training Data from Large Language Modelscited by 2,189
  11. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,190
  12. 2023ImageBind: One Embedding Space To Bind Them Allcited by 1,175
20 more
  1. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  2. 2023Membership Inference Attacks against Language Models via Neighbourhood Comparisoncited by 258
  3. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 185
  4. 2022Reconstructing Training Data from Trained Neural Networkscited by 133
  5. 2023Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Modelscited by 110
  6. 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
  7. 2020Adaptive Reward-Poisoning Attacks against Reinforcement Learningcited by 86
  8. 2023BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPTcited by 76
  9. 2023Bag of Tricks for Training Data Extraction from Language Modelscited by 70
  10. 2023CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representationscited by 69
  11. 2020LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protectioncited by 57
  12. 2023Autonomous GIS: the next-generation AI-powered GIScited by 53
  13. 2023Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Modelscited by 50
  14. 2023Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Modelscited by 44
  15. 2023AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiologycited by 34
  16. 2023Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Modelscited by 13
  17. 2023Philosophical Foundations of GeoAI: Exploring Sustainability, Diversity, and Bias in GeoAI and Spatial Data Sciencecited by 11
  18. 2023Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligencecited by 8
  19. 2023The Ethics of AI-Generated Maps: A Study of DALLE 2 and Implications for Cartographycited by 6
  20. 2023CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approachescited by 6

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 6; 2025: 4.

12 papers.

  1. 2025A Survey on Mechanistic Interpretability for Multi-Modal Foundation Modelscited by 34
  2. 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 23
  3. 2024A Survey on Safe Multi-Modal Learning Systemcited by 15
  4. 2024The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspectivecited by 15
  5. 2023Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanitiescited by 9
  6. 2024Self-Supervised Representation Learning for Geospatial Objects: A Surveycited by 9
  7. 2025Whose Truth? Pluralistic Geo-Alignment for (Agentic) AIcited by 6
  8. 2024Pre-trained Transformer Uncovers Meaningful Patterns in Human Mobility Datacited by 1
  9. 2024Evaluating the Effectiveness of Large Language Models in Representing and Understanding Movement Trajectoriescited by 1
  10. 2024Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Eventscited by 1
  11. 2023Entity Embeddings : Perspectives Towards an Omni-Modality Era for Large Language Modelscited by 0
  12. 2025Trust in foundation models and GenAI: A geographic perspectivecited by 0