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

2024-12-16

AI Summary

In GNSS-denied scenarios (e.g., disaster response, urban canyons), cross-view geo-localization suffers severe performance degradation due to extreme viewpoint disparity—specifically, significant decentrality—between street-level and satellite imagery. To address this, we propose AuxGeo: a novel framework featuring (i) the first large-scale DReSS benchmark explicitly designed for decentrality-aware evaluation; (ii) a Bird’s-eye-view Intermediate Module (BIM) that enables cross-view feature alignment and geometric disentanglement; (iii) a Position Constraint Module (PCM) that fuses dual-stream encoded features under explicit geometric priors; and (iv) multi-task metric learning jointly optimizing semantic and geometric consistency. Evaluated on DReSS, AuxGeo substantially mitigates accuracy loss induced by large decentrality. Moreover, it achieves state-of-the-art performance on three major benchmarks—CVUSA, CVACT, and VIGOR—demonstrating robust generalization across diverse cross-view localization settings.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Localization, Mapping, and Navigation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Cross-perspective Geolocalization
• High Eccentricity
• Alignment Discrepancy

Innovation

• DReSS Dataset
• AuxGeo System
• Cross-View Geolocalization