{"url":"/dataset/urban-hyperspectral-image","name":"Urban Hyperspectral Image","full_name":null,"description_markdown":"Urban is one of the most widely used hyperspectral data used in the hyperspectral unmixing study. There are 307x307 pixels, each of which corresponds to a 2x2 m2 area. In this image, there are 210 wavelengths ranging from 400 nm to 2500 nm, resulting in a spectral resolution of 10 nm. After the channels 1-4, 76, 87, 101-111, 136-153 and 198-210 are removed (due to dense water vapor and atmospheric effects), we remain 162 channels (this is a common preprocess for hyperspectral unmixing analyses). There are three versions of ground truth, which contain 4, 5 and 6 endmembers respectively, which are introduced in the ground truth.\r\n\r\nLinda S. Kalman and Edward M. Bassett III \"Classification and material identification in an urban environment using HYDICE hyperspectral data\", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.283843\r\n\r\nHosted at: \r\n- https://rslab.ut.ac.ir/data\r\n- http://lesun.weebly.com/hyperspectral-data-set.html\r\n- https://erdc-library.erdc.dren.mil/jspui/handle/11681/2925","description_withheld":null,"homepage":"http://hdl.handle.net/11681/2925","introduced_date":"2022-07-22","introduced_date_note":null,"introduced_by":null,"license":{"name":"Approved for public release; distribution is unlimited. No new capabilities are planned for HyperCube. Minimal technical support including questions & bug fixes only.","url":null},"modalities":[{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"3D Facial Landmark Localization","url":"/task/3d-facial-landmark-localization","datasets_with_task":"/datasets/task/3d-facial-landmark-localization"}],"languages":[],"variants":["Urban Hyperspectral Image"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-facial-landmark-localization-on-urban","task":"3D Facial Landmark Localization","dataset_variant":"Urban Hyperspectral Image","rows":1,"metrics":["10°5 cm"],"first_row_in_archive_order":{"model":"Lucky Brand 13","paper":"/paper/100000-podcasts-a-spoken-english-document","metrics":{"10°5 cm":"13.69"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/100000-podcasts-a-spoken-english-document","title":"100,000 Podcasts: A Spoken English Document Corpus","date":"2020-12-01","rows_on_this_dataset":1,"code_links":0,"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."}