Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse

Authors: Yunsong Fang, Tingyu Wang, Zhedong Zheng

Published in arXiv:2605.14925, 2026

Recommended citation: Yunsong Fang, Tingyu Wang, Zhedong Zheng, "Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse." arXiv:2605.14925, 2026.
Download PDF: https://zdzheng.xyz/files/2026/Roadmap_Yunsong.pdf

Code is available at: https://github.com/YsongF/GeoFuse

Abstract: Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.g., rain, snow, fog), against a gallery of geo-tagged satellite images. Weather-induced degradations in the drone view, such as noise, reduced visibility, and partial occlusions, severely exacerbate the intrinsic cross-view domain gap. While prior methods predominantly rely on weather-specific architectures or data augmentations, they have largely overlooked road map data, a readily available modality that provides strong, inherently weather-invariant geometric layout cues (e.g., road networks and building footprints) at negligible additional cost. We introduce GeoFuse, a cross-modal fusion framework that integrates precisely aligned road map tiles with satellite imagery to yield more discriminative and weather-resilient representations. We first augment the existing University-1652 and DenseUAV benchmarks with geo-aligned road maps, supplying structural priors robust to meteorological variations. Building on this, we propose a flexible fusion module that combines satellite and road map features via token-level and channel-level interactions, with a lightweight dynamic gating mechanism that adaptively weights modality contributions per instance. Finally, we employ class-level cross-view contrastive learning to promote robust alignment between weather-degraded drone features and the fused satellite-roadmap representations. Extensive experiments under diverse weather conditions show that GeoFuse consistently outperforms state-of-the-art methods, achieving +3.46\% and +23.18\% Recall@1 accuracy on the University-1652 and DenseUAV benchmarks, respectively.

@article{fang2026road,
author = "Fang, Yunsong and Wang, Tingyu and Zheng, Zhedong",
title = "Road Maps as Free Geometric Priors: Weather-Invariant Drone Geo-Localization with GeoFuse",
abstract = "Drone-view geo-localization aims to match a query drone image, often captured under adverse weather conditions (e.g., rain, snow, fog), against a gallery of geo-tagged satellite images. Weather-induced degradations in the drone view, such as noise, reduced visibility, and partial occlusions, severely exacerbate the intrinsic cross-view domain gap. While prior methods predominantly rely on weather-specific architectures or data augmentations, they have largely overlooked road map data, a readily available modality that provides strong, inherently weather-invariant geometric layout cues (e.g., road networks and building footprints) at negligible additional cost. We introduce GeoFuse, a cross-modal fusion framework that integrates precisely aligned road map tiles with satellite imagery to yield more discriminative and weather-resilient representations. We first augment the existing University-1652 and DenseUAV benchmarks with geo-aligned road maps, supplying structural priors robust to meteorological variations. Building on this, we propose a flexible fusion module that combines satellite and road map features via token-level and channel-level interactions, with a lightweight dynamic gating mechanism that adaptively weights modality contributions per instance. Finally, we employ class-level cross-view contrastive learning to promote robust alignment between weather-degraded drone features and the fused satellite-roadmap representations. Extensive experiments under diverse weather conditions show that GeoFuse consistently outperforms state-of-the-art methods, achieving +3.46\\% and +23.18\\% Recall@1 accuracy on the University-1652 and DenseUAV benchmarks, respectively.",
journal = "arXiv:2605.14925",
url = "https://zdzheng.xyz/files/2026/Roadmap\_Yunsong.pdf",
code = "https://github.com/YsongF/GeoFuse",
year = "2026" }