MM-Geo: Multi-Scale and Multi-Positive UAV-View Geo-Localization

Abstract

UAV-view geo-localization is crucial in many applications, such as material transportation and security inspection, particularly in GPS-denied urban environments. However, most existing methods assume a known drone flight altitude and divide satellite maps into tiles that approximate the scale of drone images, which are often inapplicable to real-world UAV scenarios where flight altitudes vary. In this paper, we propose a novel UAV-view geo-localization method, termed MM-Geo, to address the aforementioned issue. In particular, we partition the satellite imagery map into tiles of uniform size and retrieve the matching tiles in real time using online drone images of smaller field-of-view (FOV) at different altitudes. To address the multi-scale problem due to the varying altitudes, we design the patch vote rerank with match attention, and to tackle the multi-positive sample issue in the continuous, the normalized infoNCE loss is incorporated to provide finer supervision during contrastive learning. The proposed MM-Geo is extensively validated on the our own large-scale urban dataset MT-UAV as well as the public datasets UAV-VisLoc, outperforming the state-of-the-art (SOTA) approaches and achieving remarkable performance in practical drone delivery operations. To benefit the community, we will release the VisLoc-related code at: https://github.com/MM-Geo-2025/MM-Geo.