{"url":"/dataset/cross-view-time-dataset","name":"Cross-View Time Dataset","full_name":null,"description_markdown":"The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Every day billions of images capture this complex relationship, many of which are associated with precise time and location metadata. We propose to use these images to construct a global-scale, dynamic map of visual appearance attributes. Such a map enables fine-grained understanding of the expected appearance at any geographic location and time. Our approach integrates dense overhead imagery with location and time metadata into a general framework capable of mapping a wide variety of visual attributes. A key feature of our approach is that it requires no manual data annotation. We demonstrate how this approach can support various applications, including image-driven mapping, image geolocalization, and metadata verification.","description_withheld":null,"homepage":"https://tsalem.github.io/DynamicMaps/","introduced_date":"2020-12-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-a-dynamic-map-of-visual-appearance-1","title":"Learning a Dynamic Map of Visual Appearance","first_author":"Tawfiq Salem","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Image-Based Localization","url":"/task/image-based-localization","datasets_with_task":"/datasets/task/image-based-localization"},{"name":"Satellite Image Classification","url":"/task/satellite-image-classification","datasets_with_task":"/datasets/task/satellite-image-classification"},{"name":"Temporal Metadata Manipulation Detection","url":"/task/temporal-metadata-manipulation-detection","datasets_with_task":"/datasets/task/temporal-metadata-manipulation-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Cross-View Time Dataset"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/temporal-metadata-manipulation-detection-on","task":"Temporal Metadata Manipulation Detection","dataset_variant":"Cross-View Time Dataset","rows":1,"metrics":["2-Class Accuracy","AUC"],"first_row_in_archive_order":{"model":"DenseNet-121 - G, t, l, S (TA)","paper":"/paper/content-based-detection-of-temporal-metadata","metrics":{"2-Class Accuracy":"81.1","AUC":"0.885"},"code_links":[{"title":"rafaspadilha/timestampVerificationTIFS","url":"https://github.com/rafaspadilha/timestampVerificationTIFS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/content-based-detection-of-temporal-metadata","title":"Content-Aware Detection of Temporal Metadata Manipulation","date":"2021-03-08","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}