(MGS)$^2$-Net: Unifying Micro-Geometric Scale and Macro-Geometric Structure for Cross-View Geo-Localization

2026-02-11

AI Summary

This work addresses the challenge of cross-view geolocalization between oblique aerial images and orthorectified satellite maps, which is hindered by 3D geometric discrepancies such as building facades and scale variations. To tackle this, the authors propose a unified framework integrating Micro-scale Geometry-aware Self-Adaptation (MGSA) and Macro-scale Geometric Structure Filtering (MGSF). The MGSF module physically suppresses facade-induced interference while enhancing view-invariant planar features, complemented by a Geometry-Appearance Contrastive Distillation (GACD) loss to mitigate domain shift and occlusion effects. Evaluated on University-1652 and SUES-200, the method achieves state-of-the-art Recall@1 scores of 97.5% and 97.02%, respectively, demonstrating significant improvements over existing approaches and strong cross-dataset generalization capability.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-instance/Multi-view Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

Cross-view geo-localization (CVGL) is pivotal for GNSS-denied UAV navigation but remains brittle under the drastic geometric misalignment between oblique aerial views and orthographic satellite references. Existing methods predominantly operate within a 2D manifold, neglecting the underlying 3D geometry where view-dependent vertical facades (macro-structure) and scale variations (micro-scale) severely corrupt feature alignment. To bridge this gap, we propose (MGS)$^2$, a geometry-grounded framework. The core of our innovation is the Macro-Geometric Structure Filtering (MGSF) module. Unlike pixel-wise matching sensitive to noise, MGSF leverages dilated geometric gradients to physically filter out high-frequency facade artifacts while enhancing the view-invariant horizontal plane, directly addressing the domain shift. To guarantee robust input for this structural filtering, we explicitly incorporate a Micro-Geometric Scale Adaptation (MGSA) module. MGSA utilizes depth priors to dynamically rectify scale discrepancies via multi-branch feature fusion. Furthermore, a Geometric-Appearance Contrastive Distillation (GACD) loss is designed to strictly discriminate against oblique occlusions. Extensive experiments demonstrate that (MGS)$^2$ achieves state-of-the-art performance, recording a Recall@1 of 97.5\% on University-1652 and 97.02\% on SUES-200. Furthermore, the framework exhibits superior cross-dataset generalization against geometric ambiguity. The code is available at: \href{https://github.com/GabrielLi1473/MGS-Net}{https://github.com/GabrielLi1473/MGS-Net}.

Problem

• cross-view geo-localization
• geometric misalignment
• macro-geometric structure
• micro-geometric scale
• UAV navigation

Innovation

• Macro-Geometric Structure Filtering
• Micro-Geometric Scale Adaptation
• Cross-View Geo-Localization
• Geometric-Appearance Contrastive Distillation
• 3D Geometry Awareness