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arXiv · 2603.13628

Locatability-Guided Adaptive Reasoning for Image Geo-Localization with Vision-Language Models

Bo Yu·Fengze Yang·Yiming Liu·Chao Wang·Xuewen Luo·Taozhe Li·Ruimin Ke·Xiaofan Zhou·Chenxi Liu
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Abstract

The emergence of Vision-Language Models (VLMs) has introduced new paradigms for global image geo-localization through retrieval-augmented generation (RAG) and reasoning-driven inference. However, RAG methods are constrained by retrieval database quality, while reasoning-driven approaches fail to internalize image locatability, relying on inefficient, fixed-depth reasoning paths that increase hallucinations and degrade accuracy. To overcome these limitations, we introduce an Optimized Locatability Score that quantifies an image's suitability for deep reasoning in geo-localization. Using this metric, we curate Geo-ADAPT-51K, a locatability-stratified reasoning dataset enriched with augmented reasoning trajectories for complex visual scenes. Building on this foundation, we propose a two-stage Group Relative Policy Optimization (GRPO) curriculum with customized reward functions that regulate adaptive reasoning depth, visual grounding, and hierarchical geographical accuracy. Our framework, Geo-ADAPT, learns an adaptive reasoning policy, achieves state-of-the-art performance across multiple geo-localization benchmarks, and substantially reduces hallucinations by reasoning both adaptively and efficiently.

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Bo Yu, Fengze Yang, Yiming Liu, Chao Wang, Xuewen Luo, Taozhe Li, Ruimin Ke, Xiaofan Zhou, Chenxi Liu. 2026-03-13. Locatability-Guided Adaptive Reasoning for Image Geo-Localization with Vision-Language Models. https://arxiv.org/abs/2603.13628

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