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

TL;DR

GeoFuse introduces a weather-invariant drone geo-localization method by integrating road map data with satellite imagery, enhancing robustness against adverse weather conditions.

Contribution

The paper proposes a novel fusion framework that combines aligned road maps with satellite images, improving weather resilience in drone geo-localization tasks.

Findings

GeoFuse outperforms state-of-the-art methods on benchmarks.

Achieves +3.46% and +23.18% Recall@1 accuracy improvements.

Effectively integrates structural priors for weather-invariant localization.

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.