Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
Previous methods for image geo-localization have typically treated the task
as either classification or retrieval, often relying on black-box decisions
that lack interpretability. The rise of large vision-language models (LVLMs)
has enabled a rethinking of geo-localization as a reasoning-driven task
grounded in visual cues. However, two major challenges persist. On the data
side, existing reasoning-focused datasets are primarily based on street-view
imagery, offering limited scene diversity and constrained viewpoints. On the
modeling side, current approaches predominantly rely on supervised fine-tuning,
which yields only marginal improvements in reasoning capabilities. To address
these challenges, we propose a novel pipeline that constructs a
reasoning-oriented geo-localization dataset, MP16-Reason, using diverse social
media images. We introduce GLOBE, Group-relative policy optimization for
Locatability assessment and Optimized visual-clue reasoning, yielding
Bi-objective geo-Enhancement for the VLM in recognition and reasoning. GLOBE
incorporates task-specific rewards that jointly enhance locatability
assessment, visual clue reasoning, and geolocation accuracy. Both qualitative
and quantitative results demonstrate that GLOBE outperforms state-of-the-art
open-source LVLMs on geo-localization tasks, particularly in diverse visual
scenes, while also generating more insightful and interpretable reasoning
trajectories.
