NGC-GeoLoc: Neural GeoCoordinate Regression for GPS-Denied UAV Geo-Localization

Abstract

Visual geo-localization without GPS prior remains a significant challenge for UAV navigation. Traditional retrievalbased methods suffer from scale and rotation variances between UAV images and satellite maps, and their inference speed degrades with increasing map size. To address these challenges, we propose NGC-GeoLoc, a novel geo-coordinate regression framework designed specifically for UAV localization using satellite maps only. Our core idea is to replace retrieval by directly regressing map-space coordinates for every query pixel. Technically, our pipeline combines a visual encoder with regression head, trained with dense supervision generated from multi-scale, multi-rotation satellite crops. During inference, our method efficiently recovers the UAV pose by solving a homography using predicted dense correspondences and refining with dense feature matching. This regression-based framework enables UAV localization that is robust to rotation and scale variations with sub-second inference time, regardless of map size. In experiments on UAV-VisLoc across seven different scenes, NGC-GeoLoc surpasses the baseline in recall on seven scenes and achieves lower inference time across the board. The dataset and code will be publicly available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/djcrobo/NGC-Geoloc</uri>.