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GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
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GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
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Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the vast diversity in visual content across regions. While recent approaches adopt a two-stage pipeline of retrieving candidates and selecting the best match, they typically rely on simplistic similarity heuristics and point-wise supervision, failing to model spatial relationships among candidates. In this paper, we propose GeoRanker, a distance-aware ranking framework that leverages large vision-language models to jointly encode query-candidate interactions and predict geographic proximity. In addition, we introduce a multi-order distance loss that ranks both absolute and relative distances, enabling the model to reason over structured spatial relationships. To support this, we curate GeoRanking, the first dataset explicitly designed for geographic ranking tasks with multimodal candidate information. GeoRanker achieves state-of-the-art results on two well-established benchmarks (IM2GPS3K and YFCC4K), significantly outperforming current best methods.
Forward citations
Cited by 5 Pith papers
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VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale
VidTAG achieves fine-grained global video-to-GPS geolocalization via temporal frame alignment and denoising sequence refinement, reporting 20% gains at 1 km over GeoCLIP and 25% on CityGuessr68k.
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Skill-Conditioned Visual Geolocation for Vision-Language Models
GeoSkill uses an evolving Skill-Graph initialized from expert trajectories and grown via autonomous analysis of successful and failed reasoning rollouts to boost geolocation accuracy, faithfulness, and generalization ...
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Skill-Conditioned Visual Geolocation for Vision-Language Models
GeoSkill lets vision-language models improve geolocation accuracy and reasoning by maintaining an evolving Skill-Graph that grows through autonomous analysis of successful and failed rollouts on web-scale image data.
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HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning
A VLM geo-localizer trained with multi-cue rewards improves accuracy on a new landmark-bias benchmark, but the benchmark and training data suffer from unresolved leakage and overlap concerns.
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GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by...
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