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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.","url_abs":"https://arxiv.org/abs/2411.12431v1","url_pdf":"https://arxiv.org/pdf/2411.12431v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cv-cities-advancing-cross-view-geo","repo_url":"https://github.com/gaoshuang98/cvcities","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"cross-view-geo-localisation","task_name":"Cross-View Geo-Localisation"},{"task_slug":"drone-view-target-localization","task_name":"Drone-view target localization"},{"task_slug":"image-based-localization","task_name":"Image-Based Localization"},{"task_slug":"visual-place-recognition","task_name":"Visual Place Recognition"},{"task_slug":"geo-localization","task_name":"geo-localization"}],"methods":[{"method_slug":"infonce","method_name":"InfoNCE"}],"datasets_introduced":[{"slug":"cv-cities","name":"CV-Cities","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/drone-view-target-localization-on-university-1","task":"Drone-view target localization","dataset":"University-1652","model":"CV-Cities","rank_in_archive_order":2,"of":11,"metrics":{"AP":"95.01","Recall@1":"97.43"},"uses_additional_data":false},{"leaderboard":"/sota/image-based-localization-on-vigor-cross-area","task":"Image-Based Localization","dataset":"VIGOR Cross Area","model":"CV-Cities","rank_in_archive_order":1,"of":5,"metrics":{"Hit Rate":"75.97","Recall@1":"64.61","Recall@1%":"98.63","Recall@10":"91.20","Recall@5":"87.48"},"uses_additional_data":false},{"leaderboard":"/sota/image-based-localization-on-vigor-same-area","task":"Image-Based Localization","dataset":"VIGOR Same Area","model":"CV-Cities","rank_in_archive_order":1,"of":5,"metrics":{"Hit Rate":"90.76","Recall@1":"78.27","Recall@1%":"99.67","Recall@10":"97.52","Recall@5":"96.10"},"uses_additional_data":false},{"leaderboard":"/sota/image-based-localization-on-cvact","task":"Image-Based Localization","dataset":"cvact","model":"CV-Cities","rank_in_archive_order":1,"of":8,"metrics":{"Recall@1":"92.59","Recall@1 (%)":"98.72","Recall@10":"97.82","Recall@5":"97.16"},"uses_additional_data":false},{"leaderboard":"/sota/image-based-localization-on-cvusa-1","task":"Image-Based Localization","dataset":"cvusa","model":"CV-Cities","rank_in_archive_order":1,"of":8,"metrics":{"Recall@1":"99.19","Recall@10":"99.85","Recall@5":"99.80","Recall@top1%":"99.92"},"uses_additional_data":false},{"leaderboard":"/sota/visual-place-recognition-on-cv-cities","task":"Visual Place Recognition","dataset":"CV-Cities","model":"CV-Cities","rank_in_archive_order":1,"of":3,"metrics":{"Recall@1":"82.91","Recall@5":"90.14"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2411.12431","atlas_url":"https://app.syntology.ai/?focus=2411.12431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.12431"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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