# Image-Based Geolocation Using Large Vision-Language Models

arXiv: https://arxiv.org/abs/2408.09474 · HTML: https://mlsanity.com/paper/2408.09474/

## Key facts

- Cited by 17 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 10 of its 39 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 10 of 10)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Analyzing and Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2310.00754/) (2023)
- [GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization](https://mlsanity.com/paper/2309.16020/) (2023)
- [The StreetLearn Environment and Dataset](https://mlsanity.com/paper/1903.01292/) (2019)
- [TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection](https://mlsanity.com/paper/2403.08108/) (2024)
- [Granular Privacy Control for Geolocation with Vision Language Models](https://mlsanity.com/paper/2407.04952/) (2024)
- [Watch Your Language: Investigating Content Moderation with Large Language Models](https://mlsanity.com/paper/2309.14517/) (2023)
- [Semantic Image Based Geolocation Given a Map](https://mlsanity.com/paper/1609.00278/) (2016)

## Built on by (most cited first, 17 of 17)

- [GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization](https://mlsanity.com/paper/2505.13731/) (2025)
- [Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models](https://mlsanity.com/paper/2504.19373/) (2025)
- [GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models](https://mlsanity.com/paper/2506.01277/) (2025)
- [GeoArena: Evaluating Open-World Geographic Reasoning in Large Vision-Language Models](https://mlsanity.com/paper/2509.04334/) (2025)
- [GeoExplain: Multimodal Reasoning based on Hierarchy of Visual Information in Street View](https://mlsanity.com/paper/2506.16633/) (2025)
- [Evaluation of Geolocation Capabilities of Multimodal Large Language Models and Analysis of Associated Privacy Risks](https://mlsanity.com/paper/2506.23481/) (2025)
- [Who Can See Through You? Adversarial Shielding Against VLM-Based Attribute Inference Attacks](https://mlsanity.com/paper/2512.18264/) (2025)
- [GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents](https://mlsanity.com/paper/2511.22441/) (2025)
- [LocationAgent: A Hierarchical Agent for Image Geolocation via Decoupling Strategy and Evidence from Parametric Knowledge](https://mlsanity.com/paper/2601.19155/) (2026)
- [Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?](https://mlsanity.com/paper/2602.05023/) (2026)
- [HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning](https://mlsanity.com/paper/2607.15255/) (2026)
- [V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions](https://mlsanity.com/paper/2512.11995/) (2025)
- [GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction](https://mlsanity.com/paper/2601.13207/) (2026)
- [TimeSpot: Benchmarking Geo-Temporal Understanding in Vision-Language Models in Real-World Settings](https://mlsanity.com/paper/2603.06687/) (2026)
- [DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery](https://mlsanity.com/paper/2603.14837/) (2026)
- [GeoRouter: Dynamic Paradigm Routing for Worldwide Image Geolocalization](https://mlsanity.com/paper/2603.24376/) (2026)
- [Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models](https://mlsanity.com/paper/2609.21363/) (2026)

## Abstract (arXiv)

Geolocation is now a vital aspect of modern life, offering numerous benefits but also presenting serious privacy concerns. The advent of large vision-language models (LVLMs) with advanced image-processing capabilities introduces new risks, as these models can inadvertently reveal sensitive geolocation information. This paper presents the first in-depth study analyzing the challenges posed by traditional deep learning and LVLM-based geolocation methods. Our findings reveal that LVLMs can accurately determine geolocations from images, even without explicit geographic training. To address these challenges, we introduce tool, an innovative framework that significantly enhances image-based geolocation accuracy. tool employs a systematic chain-of-thought (CoT) approach, mimicking human geoguessing strategies by carefully analyzing visual and contextual cues such as vehicle types, architectural styles, natural landscapes, and cultural elements. Extensive testing on a dataset of 50,000 ground-truth data points shows that tool outperforms both traditional models and human benchmarks in accuracy. It achieves an impressive average score of 4550.5 in the GeoGuessr game, with an 85.37% win rate, and delivers highly precise geolocation predictions, with the closest distances as accurate as 0.3 km. Furthermore, our study highlights issues related to dataset integrity, leading to the creation of a more robust dataset and a refined framework that leverages LVLMs' cognitive capabilities to improve geolocation precision. These findings underscore tool's superior ability to interpret complex visual data, the urgent need to address emerging security vulnerabilities posed by LVLMs, and the importance of responsible AI development to ensure user privacy protection.