{"url":"/dataset/cv-cities","name":"CV-Cities","full_name":null,"description_markdown":"CV-Cities comprises $223,736$ ground panoramic images and an equal number of satellite images all accompanied by high-precision GPS coordinates. These images represent sixteen representative cities across five continents. The ground images are $360^{\\circ}$ panorama images with a resolution of $4,096 \\times 2,048$ pixels, while the resolution of satellite images is $746 \\times 746$ pixels, and are captured at a zoom level of $20$. The spatial resolution is $0.298 m$, corresponding to a latitude and longitude range of $0.002 \\times 0.002^{\\circ}$ (about $222 \\times 222 m$). The images of each city in the dataset can be used for training and testing purposes.","description_withheld":null,"homepage":"https://github.com/GaoShuang98/CVCities","introduced_date":"2024-11-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/cv-cities-advancing-cross-view-geo","title":"CV-Cities: Advancing Cross-View Geo-Localization in Global Cities","first_author":"Gaoshuang Huang","url":null},"license":{"name":"bsd-3-clause","url":"https://github.com/GaoShuang98/CVCities?tab=BSD-3-Clause-1-ov-file"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Visual Place Recognition","url":"/task/visual-place-recognition","datasets_with_task":"/datasets/task/visual-place-recognition"},{"name":"geo-localization","url":"/task/geo-localization","datasets_with_task":"/datasets/task/geo-localization"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["CV-Cities"],"data_loaders":[{"repo":"https://github.com/gaoshuang98/cvcities","url":"https://hf-mirror.com/datasets/gaoshuang98/CV-Cities","frameworks":["pytorch"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-place-recognition-on-cv-cities","task":"Visual Place Recognition","dataset_variant":"CV-Cities","rows":3,"metrics":["Recall@1","Recall@5"],"first_row_in_archive_order":{"model":"CV-Cities","paper":"/paper/cv-cities-advancing-cross-view-geo","metrics":{"Recall@1":"82.91","Recall@5":"90.14"},"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"}],"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":1,"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/sample4geo-hard-negative-sampling-for-cross","title":"Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation","date":"2023-03-21","rows_on_this_dataset":1,"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":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":10,"samples_ran":1,"samples_unverified":9,"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."}