Vision-Language Reasoning for Geolocalization: A Reinforcement Learning Approach

TL;DR

This paper introduces Geo-R, a reinforcement learning framework for image geolocalization that uses hierarchical geographic reasoning without relying on synthetic annotations or external retrieval, improving accuracy and interpretability.

Contribution

Geo-R is a novel retrieval-free, reinforcement learning-based approach that leverages structured geographic reasoning for scalable and interpretable image geolocalization.

Findings

Achieved state-of-the-art accuracy on multiple benchmarks.

Demonstrated strong generalization across diverse datasets.

Provided transparent reasoning paths for localization decisions.

Abstract

Recent advances in vision-language models have opened up new possibilities for reasoning-driven image geolocalization. However, existing approaches often rely on synthetic reasoning annotations or external image retrieval, which can limit interpretability and generalizability. In this paper, we present Geo-R, a retrieval-free framework that uncovers structured reasoning paths from existing ground-truth coordinates and optimizes geolocation accuracy via reinforcement learning. We propose the Chain of Region, a rule-based hierarchical reasoning paradigm that generates precise, interpretable supervision by mapping GPS coordinates to geographic entities (e.g., country, province, city) without relying on model-generated or synthetic labels. Building on this, we introduce a lightweight reinforcement learning strategy with coordinate-aligned rewards based on Haversine distance, enabling the model to refine predictions through spatially meaningful feedback. Our approach bridges structured geographic reasoning with direct spatial supervision, yielding improved localization accuracy, stronger generalization, and more transparent inference. Experimental results across multiple benchmarks confirm the effectiveness of Geo-R, establishing a new retrieval-free paradigm for scalable and interpretable image geolocalization. To facilitate further research and ensure reproducibility, both the model and code will be made publicly available.