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[2602.12617] GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics
Summary
GeoAgent introduces a novel model for geolocation tasks, enhancing AI's reasoning capabilities with geographic characteristics and outperforming existing methods.
Why It Matters
The development of GeoAgent addresses significant limitations in current AI geolocation methods by integrating human-like reasoning and geographic context. This advancement could improve applications in navigation, location-based services, and geographic information systems, making AI more reliable in real-world scenarios.
Key Takeaways
- GeoAgent utilizes a new dataset, GeoSeek, for training with geographic expert annotations.
- The model incorporates geo-similarity and consistency rewards to enhance reasoning accuracy.
- Experimental results indicate GeoAgent's superior performance over existing geolocation methods.
Computer Science > Artificial Intelligence arXiv:2602.12617 (cs) [Submitted on 13 Feb 2026] Title:GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics Authors:Modi Jin, Yiming Zhang, Boyuan Sun, Dingwen Zhang, MingMing Cheng, Qibin Hou View a PDF of the paper titled GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics, by Modi Jin and 5 other authors View PDF HTML (experimental) Abstract:This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions. Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies, which conflict with geographic characteristics. To address these issues, we first introduce GeoSeek, a new geolocation dataset comprising CoT data annotated by geographic experts and professional players. We further thoroughly explore the inherent characteristics of geographic tasks and propose a geo-similarity reward and a consistency reward assessed by a consistency agent to assist training. This encourages the model to converge towards correct answers from a geographic perspective while ensuring the integrity and consistency of its reasoning process. Experimental results show that GeoAgent outperforms existing methods and a series of general VLLMs across multiple grains, while generating rea...