MOGeo: Beyond One-to-One Cross-View Object Geo-localization

• URL: http://arxiv.org/abs/2603.13843v1
• Date: Sat, 14 Mar 2026 08:54:16 GMT
• Title: MOGeo: Beyond One-to-One Cross-View Object Geo-localization
• Abstract summary: Cross-View Object Geo-Localization (CVOGL) aims to locate an object of interest in a query image within a corresponding satellite image.<n>To bridge the gap between the realistic setting and existing task, we propose a new task, called Cross-View Multi-Object Geo-Localization (CVMOGL)<n>We propose a novel cross-view multi-object geo-localization method, MOGeo, and benchmark it against existing state-of-the-art methods.
• Score: 27.10845184742442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-View Object Geo-Localization (CVOGL) aims to locate an object of interest in a query image within a corresponding satellite image. Existing methods typically assume that the query image contains only a single object, which does not align with the complex, multi-object geo-localization requirements in real-world applications, making them unsuitable for practical scenarios. To bridge the gap between the realistic setting and existing task, we propose a new task, called Cross-View Multi-Object Geo-Localization (CVMOGL). To advance the CVMOGL task, we first construct a benchmark, CMLocation, which includes two datasets: CMLocation-V1 and CMLocation-V2. Furthermore, we propose a novel cross-view multi-object geo-localization method, MOGeo, and benchmark it against existing state-of-the-art methods. Extensive experiments are conducted under various application scenarios to validate the effectiveness of our method. The results demonstrate that cross-view object geo-localization in the more realistic setting remains a challenging problem, encouraging further research in this area.

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