{"url":"/dataset/hyperview","name":"HYPERVIEW","full_name":"Seeing Beyond the Visible","description_markdown":"The dataset comprises 2886 patches in total (2 m GSD), of which 1732 patches for training and 1154 patches for testing. The patch size varies (depending on agricultural parcels) and is on average around 60x60 pixels. Each patch contains 150 contiguous hyperspectral bands (462-942 nm, with a spectral resolution of 3.2 nm), which reflects the spectral range of the hyperspectral imaging sensor deployed on-board Intuition-1.\r\n\r\nThe participants are given a training set of 1732 training examples. The examples are hyperspectral image patches with the corresponding ground-truth information. Each masked patch corresponds to a field of interest. Ground truth are the soil parameters obtained for the soil samples collected for each field of interest in the process of laboratory analysis, and is represented by a 4-value vector.","description_withheld":null,"homepage":"https://platform.ai4eo.eu/seeing-beyond-the-visible/data","introduced_date":"2022-02-01","introduced_date_note":null,"introduced_by":null,"license":{"name":"Commercial/Private","url":"https://platform.ai4eo.eu/seeing-beyond-the-visible/data"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Seeing Beyond the Visible","url":"/task/seeing-beyond-the-visible","datasets_with_task":"/datasets/task/seeing-beyond-the-visible"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["HYPERVIEW"],"data_loaders":[{"repo":"https://github.com/AI4EO/kp-labs-seeing-beyond-visible-challenge","url":"https://github.com/AI4EO/kp-labs-seeing-beyond-visible-challenge","frameworks":["tf","pytorch"]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/seeing-beyond-the-visible-on-hyperview","task":"Seeing Beyond the Visible","dataset_variant":"HYPERVIEW","rows":1,"metrics":["normalized MSE"],"first_row_in_archive_order":{"model":"RF + KNN","paper":"/paper/predicting-soil-properties-from-hyperspectral","metrics":{"normalized MSE":"0.78113"},"code_links":[{"title":"ridvansalihkuzu/hyperview_eagleeyes","url":"https://github.com/ridvansalihkuzu/hyperview_eagleeyes"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/predicting-soil-properties-from-hyperspectral","title":"Predicting Soil Properties from Hyperspectral Satellite Images","date":"2022-10-18","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."}