Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization
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 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 Gemini-3-Pro with Google Search/Map grounded mode.
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Yuxiang Ji
claimed_verified
2026-01-12T10:34:41.283Z
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Yuxiang Ji
Yux1ang
user
Yux1ang
69646268138cc47cbd76527f
Yong Wang
69646268138cc47cbd765280
Ziyu Ma
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Yiming Hu
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Hailang Huang
claimed_verified
2026-01-12T10:34:39.368Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65003db8bef9b594656f8fa7/L6cvPOAeBRnFnIQwWxYyf.png
Hailang Huang
lerogo
user
lerogo
69646268138cc47cbd765283
Xuecai Hu
69646268138cc47cbd765284
Guanhua Chen
69646268138cc47cbd765285
Liaoni Wu
69646268138cc47cbd765286
Xiangxiang Chu
Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization
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 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 Gemini-3-Pro with Google Search/Map grounded mode.
Large vision-language models are enhanced for image geolocalization by incorporating map-based reasoning and agent-in-the-map loop optimization, achieving superior accuracy compared to existing models.
vision-language model geolocalization chain-of-thought reasoning agentic capabilities agentic reinforcement learning parallel test-time scaling agent-in-the-map loop MAPBench Acc@500m