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• OSGeo matches or surpasses the accuracy of leading segmentation-based methods for CVOGL.result 90%
• The proposed OSGeo framework achieves state-of-the-art performance in Cross-View Object Geo-Localization (CVOGL).result 95%
• OSGeo utilizes Rotated Bounding Boxes (RBoxes) to improve geometric fit for oriented objects in CVOGL.method 95%
• The OSGeo framework incorporates a multi-scale perception module and an orientation-sensitive head.method 95%
• The CVOGL-R dataset is the first dataset with precise RBox annotations for CVOGL.method 95%
• OSGeo achieves comparable accuracy to segmentation-based methods with over an order of magnitude lower annotation cost.result 90%
• Performance of OSGeo may degrade in low-visibility conditions.limitation 70%
• The product opportunity targets drone service providers, logistics companies, and agricultural tech firms.market 70%

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