Cross-View UAV Geo-Localization with Precision-Focused Efficient Design: A Hierarchical Distillation Approach with Multi-view Refinement

Cross-view geo-localization (CVGL) enables UAV localization by matching aerial images to geo-tagged satellite databases, which is critical for autonomous navigation in GNSS-denied environments. However, existing methods rely on resource-intensive fine-grained feature extraction and alignment, where multiple branches and modules significantly increase inference costs, limiting their deployment on edge devices. We propose Precision-Focused Efficient Design (PFED), a resource-efficient framework combining hierarchical knowledge transfer and multi-view representation refinement. This innovative method comprises two key components: 1) During training, Hierarchical Distillation paradigm for fast and accurate CVGL (HD-CVGL), coupled with Uncertainty-Aware Prediction Alignment (UAPA) to distill essential information and mitigate the data imbalance without incurring additional inference overhead. 2) During inference, an efficient Multi-view Refinement Module (MRM) leverages mutual information to filter redundant samples and effectively utilize the multi-view data. Extensive experiments show that PFED achieves state-of-the-art performance in both accuracy and efficiency, reaching 97.15\% Recall@1 on University-1652 while being over $5 \times$ more efficient in FLOPs and $3 \times$ faster than previous top methods. Furthermore, PFED runs at 251.5 FPS on the AGX Orin edge device, demonstrating its practical viability for real-time UAV applications. The project is available at https://github.com/SkyEyeLoc/PFED