Towards Interactive Global Geolocation Assistant

Global geolocation, the task of predicting precise coordi-nates from street-view imagery, is inherently plagued by visual ambi-guity. Resolving such ambiguity necessitates a transition from static,one-shot predictions to an interactive geolocation paradigm drivenby multi-turn deductive reasoning. However, most of existing geoloca-tion models and general-purpose MLLMs fail to support this dynamicprocess, mainly due to the lack of interaction capabilities and the ge-ographic knowledge gap. Driven by the imperative to actualize thisinteractive paradigm, we introduce MG-Geo, the first large-scale mul-timodal geolocation dataset explicitly structured for spatial reasoning.Comprising 4.87M geo-tagged Meta entries, 70K image-grounded Cluesamples, and 73K multi-turn Dialog samples across 210 countries andterritories, MG-Geo separates large-scale geographic alignment fromreasoning-oriented supervision. Experiments demonstrate that GaGAachieves SOTA performance across several benchmarks. Notably, on theGWS15k dataset, it surpasses the strong Hybrid Model by 4.57% and2.92% at the country and city levels, respectively, while securing thehighest city-level accuracy (7.46%) on OSV-5M-test. More importantly,we formalize the “Similarity Trap”—a phenomenon where distributivevisual features mislead static models—and demonstrate that GaGA ef-fectively navigates this challenge through a Tiered Interaction Protocol.By dynamically integrating user-provided geographic anchors, GaGAachieves significant localization improvements. Our dataset is accessiblevia: https://huggingface.co/datasets/kendouvg/MG-Geo.