Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings

TL;DR

This paper introduces a new dataset and method for cross-view geo-localization that effectively handles decentrality issues, improving localization accuracy in challenging real-world scenarios with large geographic offsets.

Contribution

The paper presents DReSS, a novel dataset emphasizing decentrality, and AuxGeo, a new method with modules that enhance localization accuracy under decentrality conditions.

Findings

AuxGeo outperforms previous methods on DReSS and public datasets.

The proposed modules mitigate accuracy decline caused by decentrality.

DReSS provides a challenging benchmark for decentrality in geo-localization.

Abstract

Cross-View Geo-Localization tackles the challenge of image geo-localization in GNSS-denied environments, including disaster response scenarios, urban canyons, and dense forests, by matching street-view query images with geo-tagged aerial-view reference images. However, current research often relies on benchmarks and methods that assume center-aligned settings or account for only limited decentrality, which we define as the offset of the query image relative to the reference image center. Such assumptions fail to reflect real-world scenarios, where reference databases are typically pre-established without the possibility of ensuring perfect alignment for each query image. Moreover, decentrality is a critical factor warranting deeper investigation, as larger decentrality can substantially improve localization efficiency but comes at the cost of declines in localization accuracy. To address this limitation, we introduce DReSS (Decentrality Related Street-view and Satellite-view dataset), a novel dataset designed to evaluate cross-view geo-localization with a large geographic scope and diverse landscapes, emphasizing the decentrality issue. Meanwhile, we propose AuxGeo (Auxiliary Enhanced Geo-Localization) to further study the decentrality issue, which leverages a multi-metric optimization strategy with two novel modules: the Bird's-eye view Intermediary Module (BIM) and the Position Constraint Module (PCM). These modules improve the localization accuracy despite the decentrality problem. Extensive experiments demonstrate that AuxGeo outperforms previous methods on our proposed DReSS dataset, mitigating the issue of large decentrality, and also achieves state-of-the-art performance on existing public datasets such as CVUSA, CVACT, and VIGOR.