CV-Cities: Advancing Cross-View Geo-Localization in Global Cities

• URL: http://arxiv.org/abs/2411.12431v1
• Date: Tue, 19 Nov 2024 11:41:22 GMT
• Title: CV-Cities: Advancing Cross-View Geo-Localization in Global Cities
• Authors: Gaoshuang Huang, Yang Zhou, Luying Zhao, Wenjian Gan,
• Abstract summary: Cross-view geo-localization (CVGL) involves matching and retrieving satellite images to determine the geographic location of a ground image. This task faces significant challenges due to substantial viewpoint discrepancies, the complexity of localization scenarios, and the need for global localization. We propose a novel CVGL framework that integrates the foundational model DINOv2 with an advanced feature mixer.
• Score: 3.074201632920997
• License:
• Abstract: Cross-view geo-localization (CVGL), which involves matching and retrieving satellite images to determine the geographic location of a ground image, is crucial in GNSS-constrained scenarios. However, this task faces significant challenges due to substantial viewpoint discrepancies, the complexity of localization scenarios, and the need for global localization. To address these issues, we propose a novel CVGL framework that integrates the vision foundational model DINOv2 with an advanced feature mixer. Our framework introduces the symmetric InfoNCE loss and incorporates near-neighbor sampling and dynamic similarity sampling strategies, significantly enhancing localization accuracy. Experimental results show that our framework surpasses existing methods across multiple public and self-built datasets. To further improve globalscale performance, we have developed CV-Cities, a novel dataset for global CVGL. CV-Cities includes 223,736 ground-satellite image pairs with geolocation data, spanning sixteen cities across six continents and covering a wide range of complex scenarios, providing a challenging benchmark for CVGL. The framework trained with CV-Cities demonstrates high localization accuracy in various test cities, highlighting its strong globalization and generalization capabilities. Our datasets and codes are available at https://github.com/GaoShuang98/CVCities.

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