Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models

• URL: http://arxiv.org/abs/2506.14674v1
• Date: Tue, 17 Jun 2025 16:07:58 GMT
• Title: Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
• Authors: Ling Li, Yao Zhou, Yuxuan Liang, Fugee Tsung, Jiaheng Wei,
• Abstract summary: New pipeline constructs reasoning-oriented geo-localization dataset, MP16-Reason, using diverse social media images.<n>We introduce GLOBE, Group-relative policy optimization for Locatability assessment and optimized visual-clue reasoning.<n>Results demonstrate that GLOBE outperforms state-of-the-art open-source LVLMs on geo-localization tasks.
• Score: 27.848962405476108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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