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To develop a unified framework for cross-view geo-localization that enhances retrieval and metric localization tasks.

December 21, 2025Open Access

A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios

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To develop a unified framework for cross-view geo-localization that enhances retrieval and metric localization tasks.
Propose UnifyGeo, integrating retrieval and metric localization tasks into one framework.
Employ a unified learning strategy with shared parameters for multi-granularity representation.
Design a re-ranking mechanism guided by a loss function to improve localization performance.
Significant improvement in localization recall rates on the VIGOR benchmark.
1-meter-level localization recall improved from 1.53% to 39.64% under same-area evaluations.
Recall improved from 0.43% to 25.58% under cross-area evaluations.

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A unified hierarchical framework improves geo-localization accuracy in large-scale settings, enhancing performance with a novel loss function.

Song et al. (2025) studied this question.

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A unified hierarchical framework improves geo-localization accuracy in large-scale settings, enhancing performance with a novel loss function.