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arXiv AI By Yifan Zhang, Kai Wang

GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims

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GeoContext is a new vision‑language geolocation benchmark that introduces two tasks: GeoHint, where a model must localize an image given a coarse location hint, and GeoVerify, where a model must decide if an image was taken within 150 m of a claimed place. The benchmark builds a context ladder by stratifying nearby reference points by distance and referenceability, allowing the same image to be evaluated under varying context. Evaluation of five models on 109 sites in 30 cities shows that hint repetition is low, localization error grows with hint distance, and models struggle to achieve high discriminability in GeoVerify, with many false acceptances reported with high confidence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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