{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vigor-cross-view-image-geo-localization","title":"VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval","arxiv_id":"2011.12172","date":"2020-11-24","proceeding":"CVPR 2021 1","authors":["Sijie Zhu","Taojiannan Yang","Chen Chen"],"abstract":"Cross-view image geo-localization aims to determine the locations of street-view query images by matching with GPS-tagged reference images from aerial view. Recent works have achieved surprisingly high retrieval accuracy on city-scale datasets. However, these results rely on the assumption that there exists a reference image exactly centered at the location of any query image, which is not applicable for practical scenarios. In this paper, we redefine this problem with a more realistic assumption that the query image can be arbitrary in the area of interest and the reference images are captured before the queries emerge. This assumption breaks the one-to-one retrieval setting of existing datasets as the queries and reference images are not perfectly aligned pairs, and there may be multiple reference images covering one query location. To bridge the gap between this realistic setting and existing datasets, we propose a new large-scale benchmark -- VIGOR -- for cross-View Image Geo-localization beyond One-to-one Retrieval. We benchmark existing state-of-the-art methods and propose a novel end-to-end framework to localize the query in a coarse-to-fine manner. Apart from the image-level retrieval accuracy, we also evaluate the localization accuracy in terms of the actual distance (meters) using the raw GPS data. Extensive experiments are conducted under different application scenarios to validate the effectiveness of the proposed method. The results indicate that cross-view geo-localization in this realistic setting is still challenging, fostering new research in this direction. Our dataset and code will be released at \\url{https://github.com/Jeff-Zilence/VIGOR}","url_abs":"https://arxiv.org/abs/2011.12172v2","url_pdf":"https://arxiv.org/pdf/2011.12172v2.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":"vigor-cross-view-image-geo-localization","repo_url":"https://github.com/Jeff-Zilence/VIGOR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"cross-view-geo-localisation","task_name":"Cross-View Geo-Localisation"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"image-based-localization","task_name":"Image-Based Localization"},{"task_slug":"geo-localization","task_name":"geo-localization"}],"methods":[{"method_slug":"gps","method_name":"GPS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2011.12172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.12172"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Jeff-Zilence/VIGOR","reach":null}],"summary":{"ran_fixture":1,"ran_violates":1,"ran_honours":1,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"58edbd5f847f0b97","entry":"Lat_Lng","repo":"Jeff-Zilence/VIGOR","repo_kind":"official","path":"evaluate_from_npy.py","file_url":"https://github.com/Jeff-Zilence/VIGOR/blob/HEAD/evaluate_from_npy.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"58edbd5f847f0b97"}},{"code_sha256_prefix":"a36cbdbad9afc4b2","entry":"gps2distance","repo":"Jeff-Zilence/VIGOR","repo_kind":"official","path":"evaluate_from_npy.py","file_url":"https://github.com/Jeff-Zilence/VIGOR/blob/HEAD/evaluate_from_npy.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a36cbdbad9afc4b2"}},{"code_sha256_prefix":"e6f037abe00e4e38","entry":"validate","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"e6f037abe00e4e38"}},{"code_sha256_prefix":"cab3e1e87fe4a45d","entry":"validate_top","repo":"Jeff-Zilence/VIGOR","repo_kind":"official","path":"train_overall.py","file_url":"https://github.com/Jeff-Zilence/VIGOR/blob/HEAD/train_overall.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"cab3e1e87fe4a45d"}},{"code_sha256_prefix":"7b43e945d145e59c","entry":"generate_distances","repo":"Jeff-Zilence/VIGOR","repo_kind":"official","path":"evaluate_from_npy.py","file_url":"https://github.com/Jeff-Zilence/VIGOR/blob/HEAD/evaluate_from_npy.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7b43e945d145e59c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}