The image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues. Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans -- using maps. In this work, we first equip the model \textit{Thinking with Map} ability and formulate it as an agent-in-the-map loop. We develop a two-stage optimization scheme for it, including agentic reinforcement learning (RL) followed by parallel test-time scaling (TTS). The RL strengthens the agentic capability of model to improve sampling efficiency, and the parallel TTS enables the model to explore multiple candidate paths before making the final prediction, which is crucial for geolocalization. To evaluate our method on up-to-date and in-the-wild images, we further present MAPBench, a comprehensive geolocalization training and evaluation benchmark composed entirely of real-world images. Experimental results show that our method outperforms existing open- and closed-source models on most metrics, specifically improving Acc@500m from 8.0\% to 22.1\% compared to \textit{Gemini-3-Pro} with Google Search/Map grounded mode.

翻译：图像地理定位任务旨在利用视觉线索预测图像在地球上任意位置的拍摄地点。现有的大型视觉语言模型方法利用了世界知识、思维链推理和智能体能力，但忽视了人类常用的策略——使用地图。在本研究中，我们首先为模型赋予"基于地图的思考"能力，并将其形式化为地图内智能体循环框架。为此我们开发了一种两阶段优化方案，包括智能体强化学习阶段和并行测试时扩展阶段。强化学习增强了模型的智能体能力以提高采样效率，而并行测试时扩展使模型能在最终预测前探索多条候选路径，这对地理定位至关重要。为在最新真实场景图像上评估我们的方法，我们进一步提出了MAPBench——一个完全由真实世界图像构成的综合性地理定位训练与评估基准。实验结果表明，我们的方法在多数指标上超越了现有开源和闭源模型，特别是在与具备谷歌搜索/地图定位模式的Gemini-3-Pro对比时，将Acc@500m指标从8.0%提升至22.1%。