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

• URL: http://arxiv.org/abs/2505.07622v1
• Date: Mon, 12 May 2025 14:44:31 GMT
• Title: A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios
• Authors: Zhuo Song, Ye Zhang, Kunhong Li, Longguang Wang, Yulan Guo,
• Abstract summary: Cross-view geo-localization is a promising solution for large-scale localization problems.<n>We propose UnifyGeo, a novel unified hierarchical geo-localization framework.<n>We show that UnifyGeo significantly outperforms the state-of-the-arts in both task-isolated and task-associated settings.
• Score: 43.8734658237949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metric localization tasks to achieve fine-grained predictions. However, existing methods typically focus on designing standalone models for these two tasks, resulting in inefficient collaboration and increased training overhead. In this paper, we propose UnifyGeo, a novel unified hierarchical geo-localization framework that integrates retrieval and metric localization tasks into a single network. Specifically, we first employ a unified learning strategy with shared parameters to jointly learn multi-granularity representation, facilitating mutual reinforcement between these two tasks. Subsequently, we design a re-ranking mechanism guided by a dedicated loss function, which enhances geo-localization performance by improving both retrieval accuracy and metric localization references. Extensive experiments demonstrate that UnifyGeo significantly outperforms the state-of-the-arts in both task-isolated and task-associated settings. Remarkably, on the challenging VIGOR benchmark, which supports fine-grained localization evaluation, the 1-meter-level localization recall rate improves from 1.53\% to 39.64\% and from 0.43\% to 25.58\% under same-area and cross-area evaluations, respectively. Code will be made publicly available.

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