{"url":"/dataset/vigor","name":"VIGOR","full_name":null,"description_markdown":"Similar to CVUSA and CVACT, the VIGOR dataset contains satellites and street imagery to match them to each other to find the location of the street imagery. For this purpose, data from 4 major American cities were used, namely San Francisco, New York, Seattle and Chicago. Unlike the previous datasets, there are two settings: The SAME-Area setting where images of all cities are available in training and validation split. Secondly, there is the CROSS area setting where training is done on two cities (New York, Seattle) and evaluation is done on Chicago and San Francisco. In addition, the dataset contains semi-positive images which are very close to an actual ground truth image and thus serve as a distraction for the matching task. In total, the dataset consists of 90,618 satellite images and 105,214 street images.","description_withheld":null,"homepage":"https://github.com/Jeff-Zilence/VIGOR","introduced_date":"2020-11-24","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Cross-View Geo-Localisation","url":"/task/cross-view-geo-localisation","datasets_with_task":"/datasets/task/cross-view-geo-localisation"},{"name":"Image-Based Localization","url":"/task/image-based-localization","datasets_with_task":"/datasets/task/image-based-localization"}],"languages":[],"variants":["VIGOR","VIGOR Same Area","VIGOR Cross Area","VIGOR-Graph"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-based-localization-on-vigor-cross-area","task":"Image-Based Localization","dataset_variant":"VIGOR Cross Area","rows":5,"metrics":["Recall@1","Recall@5","Recall@10","Recall@1%","Hit Rate"],"first_row_in_archive_order":{"model":"CV-Cities","paper":"/paper/cv-cities-advancing-cross-view-geo","metrics":{"Hit Rate":"75.97","Recall@1":"64.61","Recall@1%":"98.63","Recall@10":"91.20","Recall@5":"87.48"},"code_links":[{"title":"gaoshuang98/cvcities","url":"https://github.com/gaoshuang98/cvcities"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-based-localization-on-vigor-same-area","task":"Image-Based Localization","dataset_variant":"VIGOR Same Area","rows":5,"metrics":["Recall@1","Recall@5","Recall@10","Recall@1%","Hit Rate"],"first_row_in_archive_order":{"model":"CV-Cities","paper":"/paper/cv-cities-advancing-cross-view-geo","metrics":{"Hit Rate":"90.76","Recall@1":"78.27","Recall@1%":"99.67","Recall@10":"97.52","Recall@5":"96.10"},"code_links":[{"title":"gaoshuang98/cvcities","url":"https://github.com/gaoshuang98/cvcities"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/cross-view-geo-localisation-on-vigor-graph","task":"Cross-View Geo-Localisation","dataset_variant":"VIGOR-Graph","rows":1,"metrics":["Accuracy (Top-1)"],"first_row_in_archive_order":{"model":"SpaGBOL","paper":"/paper/spagbol-spatial-graph-based-orientated","metrics":{"Accuracy (Top-1)":"31.88"},"code_links":[{"title":"tavisshore/SpaGBOL","url":"https://github.com/tavisshore/SpaGBOL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cv-cities-advancing-cross-view-geo","title":"CV-Cities: Advancing Cross-View Geo-Localization in Global Cities","date":"2024-11-19","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spagbol-spatial-graph-based-orientated","title":"SpaGBOL: Spatial-Graph-Based Orientated Localisation","date":"2024-09-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sample4geo-hard-negative-sampling-for-cross","title":"Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation","date":"2023-03-21","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/simple-effective-and-general-a-new-backbone","title":"Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization","date":"2023-02-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/transgeo-transformer-is-all-you-need-for","title":"TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization","date":"2022-03-31","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spatial-aware-feature-aggregation-for-image","title":"Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization","date":"2019-12-01","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":13,"samples_ran":2,"samples_unverified":11,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}