mlsanityData to 14 Oct 2026

Evaluating Precise Geolocation Inference Capabilities of Vision Language Models

arXiv 2502.14412 ↗First posted 20 Feb 2025cs.CV cs.CR cs.LGcited by 12

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

The prevalence of Vision-Language Models (VLMs) raises important questions about privacy in an era where visual information is increasingly available. While foundation VLMs demonstrate broad knowledge and learned capabilities, we specifically investigate their ability to infer geographic location from previously unseen image data. This paper introduces a benchmark dataset collected from Google Street View that represents its global distribution of coverage. Foundation models are evaluated on single-image geolocation inference, with many achieving median distance errors of <300 km. We further evaluate VLM "agents" with access to supplemental tools, observing up to a 30.6% decrease in distance error. Our findings establish that modern foundation VLMs can act as powerful image geolocation tools, without being specifically trained for this task. When coupled with increasing accessibility of these models, our findings have greater implications for online privacy. We discuss these risks, as well as future work in this area.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 12 of its 16 references that are in the map. First posted on arXiv 20 Feb 2025.

Builds on

12 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild30 May

Other references · 11

  1. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,614
  2. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 703
  3. 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 175
  4. 2022Rethinking Visual Geo-localization for Large-Scale Applicationscited by 163
  5. 2024A Survey on Benchmarks of Multimodal Large Language Modelscited by 82
  6. 2023PIGEON: Predicting Image Geolocationscited by 56
  7. 2023Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenescited by 55
  8. 2024Private Attribute Inference from Images with Vision-Language Modelscited by 37
  9. 2023Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoningcited by 22
  10. 2018DeepGeo: Photo Localization with Deep Neural Networkcited by 8
  11. 2022Privacy Concerns Raised by Pervasive User Data Collection From Cyberspace and Their Countermeasurescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 6.

12 papers.

  1. 2025GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalizationcited by 19
  2. 2025GeoChain: Multimodal Chain-of-Thought for Geographic Reasoningcited by 16
  3. 2025Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Modelscited by 14
  4. 2025GeoShield: Safeguarding Geolocation Privacy from Vision-Language Models via Adversarial Perturbationscited by 5
  5. 2025GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Modelscited by 5
  6. 2025Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attackscited by 3
  7. 2026Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?cited by 1
  8. 2026Where Do Images Come From? Analyzing Captions to Geographically Profile Datasetscited by 1
  9. 2026TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settingscited by 0
  10. 2026GeoRouter: Dynamic Paradigm Routing for Worldwide Image Geolocalizationcited by 0
  11. 2026GeoAgent: Evaluating VLM Geolocalization Through Embodied Navigationcited by 0
  12. 2026Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Granular Privacy Control for Geolocation with Vision Language Models
  2. 2025Assessing the Geolocation Capabilities, Limitations and Societal Risks of Generative Vision-Language Models
  3. 2025Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks
  4. 2025GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
  5. 2025Toward Reliable VLM: A Fine-Grained Benchmark and Framework for Exposure, Bias, and Inference in Korean Street Views
  6. 2024Image-Based Geolocation Using Large Vision-Language Models
  7. 2026GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation
  8. 2025Where on Earth? A Vision-Language Benchmark for Probing Model Geolocation Skills Across Scales