Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization
Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu

TL;DR
This paper introduces a novel map-augmented agentic approach for image geolocalization, leveraging reinforcement learning and parallel testing to improve location prediction accuracy on real-world images.
Contribution
It proposes a new agent-in-the-map framework with a two-stage optimization, including reinforcement learning and parallel test-time scaling, for enhanced geolocalization.
Findings
Outperforms existing models on most metrics.
Significantly improves Acc@500m from 8.0% to 22.1%.
Introduces MAPBench, a new real-world image geolocalization benchmark.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques · Advanced Neural Network Applications
