SMGeo: Cross-View Object Geo-Localization with Grid-Level Mixture-of-Experts

• URL: http://arxiv.org/abs/2511.14093v1
• Date: Tue, 18 Nov 2025 03:21:20 GMT
• Title: SMGeo: Cross-View Object Geo-Localization with Grid-Level Mixture-of-Experts
• Authors: Fan Zhang, Haoyuan Ren, Fei Ma, Qiang Yin, Yongsheng Zhou,
• Abstract summary: Cross-view object Geo-localization aims to precisely pinpoint the same object across large-scale satellite imagery based on drone images.<n>We present SMGeo, a promptable end-to-end transformer-based model for object Geo-localization.
• Score: 4.521626189942935
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cross-view object Geo-localization aims to precisely pinpoint the same object across large-scale satellite imagery based on drone images. Due to significant differences in viewpoint and scale, coupled with complex background interference, traditional multi-stage "retrieval-matching" pipelines are prone to cumulative errors. To address this, we present SMGeo, a promptable end-to-end transformer-based model for object Geo-localization. This model supports click prompting and can output object Geo-localization in real time when prompted to allow for interactive use. The model employs a fully transformer-based architecture, utilizing a Swin-Transformer for joint feature encoding of both drone and satellite imagery and an anchor-free transformer detection head for coordinate regression. In order to better capture both inter-modal and intra-view dependencies, we introduce a grid-level sparse Mixture-of-Experts (GMoE) into the cross-view encoder, allowing it to adaptively activate specialized experts according to the content, scale and source of each grid. We also employ an anchor-free detection head for coordinate regression, directly predicting object locations via heat-map supervision in the reference images. This approach avoids scale bias and matching complexity introduced by predefined anchor boxes. On the drone-to-satellite task, SMGeo achieves leading performance in accuracy at IoU=0.25 and mIoU metrics (e.g., 87.51%, 62.50%, and 61.45% in the test set, respectively), significantly outperforming representative methods such as DetGeo (61.97%, 57.66%, and 54.05%, respectively). Ablation studies demonstrate complementary gains from shared encoding, query-guided fusion, and grid-level sparse mixture-of-experts.

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