{"url":"/dataset/nii-cu-mapd","name":"NII-CU MAPD","full_name":"NII-CU Multispectral Aerial Person Detection Dataset","description_markdown":"The National Institute of Informatics - Chiba University (NII-CU) Multispectral Aerial Person Detection Dataset consists of 5,880 pairs of aligned RGB+FIR (Far infrared) images captured from a drone flying at heights between 20 and 50 meters, with the cameras pointed at 45 degrees down. We applied lens distortion correction and a homography warping to align the thermal images with the RGB images. We then labeled the people visible on the images with rectangular bounding boxes. The footage shows a baseball field and surroundings in Chiba, Japan, recorded in January 2020.\r\n\r\n    *RGB images captured with a Zenmuse X3 (3840x2160)\r\n    *Thermal images captured with a FLIR Vue Pro 640 in White Hot palette (640x512 before alignment)\r\n    *RGB images are corrected for lens distortion\r\n    *Thermal images are corrected for lens distortion and warped to be aligned with RGB images\r\n    *Labels are provided in CSV format","description_withheld":null,"homepage":"https://www.nii-cu-multispectral.org/","introduced_date":"2022-05-31","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-learning-with-rgb-and-thermal-images","title":"Deep learning with RGB and thermal images onboard a drone for monitoring operations","first_author":"Simon Speth","url":null},"license":{"name":"CC BY-NC-ND 4.0","url":"https://creativecommons.org/licenses/by-nc-nd/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"},{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Multispectral Object Detection","url":"/task/multispectral-object-detection","datasets_with_task":"/datasets/task/multispectral-object-detection"},{"name":"Thermal Infrared Pedestrian Detection","url":"/task/thermal-infrared-pedestrian-detection","datasets_with_task":"/datasets/task/thermal-infrared-pedestrian-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NII-CU MAPD"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multispectral-object-detection-on-nii-cu-mapd","task":"Multispectral Object Detection","dataset_variant":"NII-CU MAPD","rows":2,"metrics":["mAP@0.5:0.95","AP@0.5","AP@0.75"],"first_row_in_archive_order":{"model":"YOLOv3-4‐channel","paper":"/paper/deep-learning-with-rgb-and-thermal-images","metrics":{"AP@0.5":"97.9","AP@0.75":"76.9 ","mAP@0.5:0.95":"64.4 "},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-detection-on-nii-cu-mapd","task":"Object Detection","dataset_variant":"NII-CU MAPD","rows":1,"metrics":["mAP@0.5:0.95","AP@0.5","AP@0.75"],"first_row_in_archive_order":{"model":"YOLOv3","paper":"/paper/deep-learning-with-rgb-and-thermal-images","metrics":{"AP@0.5":"92.4","AP@0.75":"44.5","mAP@0.5:0.95":"48.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deep-learning-with-rgb-and-thermal-images","title":"Deep learning with RGB and thermal images onboard a drone for monitoring operations","date":"2022-05-31","rows_on_this_dataset":3,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}