# GeoLocator: a location-integrated large multimodal model for inferring geo-privacy

arXiv: https://arxiv.org/abs/2311.13018 · HTML: https://mlsanity.com/paper/2311.13018/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding](https://mlsanity.com/paper/2306.17107/) (2023)
- [K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization](https://mlsanity.com/paper/2306.05064/) (2023)
- [GPT4GEO: How a Language Model Sees the World's Geography](https://mlsanity.com/paper/2306.00020/) (2023)
- [GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding](https://mlsanity.com/paper/2310.14478/) (2023)
- [Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages](https://mlsanity.com/paper/2310.09340/) (2023)
- [Are Large Language Models Geospatially Knowledgeable?](https://mlsanity.com/paper/2310.13002/) (2023)
- [More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence](https://mlsanity.com/paper/2008.01916/) (2020)
- [LLM-driven Multimodal Target Volume Contouring in Radiation Oncology](https://mlsanity.com/paper/2311.01908/) (2023)
- [Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects](https://mlsanity.com/paper/2311.02332/) (2023)
- [Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images](https://mlsanity.com/paper/2311.01064/) (2023)
- [More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problems](https://mlsanity.com/paper/2311.04926/) (2023)
- [Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review](https://mlsanity.com/paper/2311.01918/) (2023)
- [Evaluating Large Language Models in Ophthalmology](https://mlsanity.com/paper/2311.04933/) (2023)

## Built on by (most cited first, 13 of 13)

- [AI Sees Your Location, But With A Bias Toward The Wealthy World](https://mlsanity.com/paper/2502.11163/) (2025)
- [Doxing via the Lens: Revealing Location-related Privacy Leakage on Multi-modal Large Reasoning Models](https://mlsanity.com/paper/2504.19373/) (2025)
- [Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models](https://mlsanity.com/paper/2504.09066/) (2025)
- [Beyond Data Privacy: New Privacy Risks for Large Language Models](https://mlsanity.com/paper/2509.14278/) (2025)
- [GeoShield: Safeguarding Geolocation Privacy from Vision-Language Models via Adversarial Perturbations](https://mlsanity.com/paper/2508.03209/) (2025)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents](https://mlsanity.com/paper/2511.22441/) (2025)
- [Georeferencing complex relative locality descriptions with large language models](https://mlsanity.com/paper/2512.14228/) (2025)
- [Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?](https://mlsanity.com/paper/2602.05023/) (2026)
- [RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery](https://mlsanity.com/paper/2606.21819/) (2026)
- [Which private attributes do VLMs agree on and predict well?](https://mlsanity.com/paper/2602.07931/) (2026)
- [DamageArbiter: A Multimodal Arbitration Framework for Disaster Damage Assessment from Street-View Imagery](https://mlsanity.com/paper/2603.14837/) (2026)
- [Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions](https://mlsanity.com/paper/2607.03233/) (2026)

## Abstract (arXiv)

Geographic privacy or geo-privacy refers to the keeping private of one's geographic location, especially the restriction of geographical data maintained by personal electronic devices. Geo-privacy is a crucial aspect of personal security; however, it often goes unnoticed in daily activities. With the surge in the use of Large Multimodal Models (LMMs), such as GPT-4, for Open Source Intelligence (OSINT), the potential risks associated with geo-privacy breaches have intensified. This study develops a location-integrated GPT-4 based model named GeoLocator and designs four-dimensional experiments to demonstrate its capability in inferring the locational information of input imageries and/or social media contents. Our experiments reveal that GeoLocator generates specific geographic details with high accuracy and consequently embeds the risk of the model users exposing geospatial information to the public unintentionally, highlighting the thread of online data sharing, information gathering technologies and LLMs on geo-privacy. We conclude with the broader implications of GeoLocator and our findings for individuals and the community at large, by emphasizing the urgency for enhanced awareness and protective measures against geo-privacy leakage in the era of advanced AI and widespread social media usage.