{"url":"/dataset/tecnalia-weee-hyperspectral-dataset","name":"Tecnalia WEEE HYPERSPECTRAL DATASET","full_name":"TECNALIA WEEE (Waste from Electrical and Electronic Equipment) HYPERSPECTRAL DATASET","description_markdown":"Tecnalia Hyperspectral Dataset  contains different non-ferreous fractions of Waste from Electric and Electronic Equipment (WEEE) of Copper, Brass, Aluminum, Stainless Steel and White Copper. Images were captured by a hyperspectral Specim PHF Fast10 camera that is able to capture wavelengths in the range 400 to 1000 nm with a spectral resolution of less than 1 nm. The PHF Fast10 camera is equipped with a CMOS sensor (1024 × 1024 resolution), a Camera Link interface and a special Fore objective OL10. The provided dataset contains 76 uniformly distributed wave-lengths in the spectral range [415.05 nm, 1008.10 nm]. Illumination setup, as described in \\cite{picon2012real}, was specifically designed to reduce the specular reflections generated by the surface of the non-ferrous materials and to provide a homogeneous and even illumination that covers the wavelengths sensitive to the hyperspectral camera. The illumination system consists of a parabolic surface that uniformly distributes the light generated by 9 halogens and 18 white LEDs covering the spectral range between 400 to 1000 nm.","description_withheld":null,"homepage":"https://zenodo.org/records/12565131","introduced_date":"2024-07-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/hyperspectral-dataset-and-deep-learning","title":"Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE)","first_author":"Artzai Picon","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"}],"tasks":[{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"Hyperspectral Image Segmentation","url":"/task/hyperspectral-image-segmentation","datasets_with_task":"/datasets/task/hyperspectral-image-segmentation"},{"name":"Hyperspectral Image Classification","url":"/task/hyperspectral-image-classification","datasets_with_task":"/datasets/task/hyperspectral-image-classification"},{"name":"Hyperspectral image analysis","url":"/task/hyperspectral-image-analysis","datasets_with_task":"/datasets/task/hyperspectral-image-analysis"}],"languages":[],"variants":["Tecnalia WEEE HYPERSPECTRAL DATASET"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}