{"url":"/dataset/okutama-drone-and-swiss-drone-dataset","name":"Okutama Drone and Swiss Drone Dataset","full_name":null,"description_markdown":"The Swiss Drone data set was recorded around Cheseaux-sur-Lausanne in Switzerland using a senseFly eBee Classic in 2014 (SenseFly, 2020). The 100 images were captured from a top-down perspective at a flight height of approximately 80 m above the ground at a resolution of 4608  x 3456 pixels. The Okutama Drone data set was recorded and annotated by NII (Laurmaa, 2016) in 2016 using a DJI Phantom 4 at a resolution of 3840 x 2160 pixels. The 91 images were captured over Okutama, west of Tokyo, Japan, from a drone at a flight height of approximately 90 m above the ground. Here, the flight height may have varied more as Okutama is located in a narrow valley with uneven ground.\r\n\r\n    *Swiss images captured with a senseFly eBee Classic in 2014 (16MP 4608 x 3456 resolution)\r\n    *Okutama images captured with a DJI Phantom 4 in June 2016 (4K 3840 x 2160 resolution)\r\n    *Labels are provided in PNG pixel-wise mask files\r\n    *9 different classes","description_withheld":null,"homepage":"https://www.okutama-segmentation.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":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Okutama Drone and Swiss Drone Dataset"],"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/semantic-segmentation-on-okutama-drone-and","task":"Semantic Segmentation","dataset_variant":"Okutama Drone and Swiss Drone Dataset","rows":4,"metrics":["mIoU","Acc"],"first_row_in_archive_order":{"model":"DeepLabv3+‐ResNet‐101","paper":"/paper/deep-learning-with-rgb-and-thermal-images","metrics":{"Acc":"90.78","mIoU":"65.88"},"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":4,"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."}