{"url":"/dataset/flair-french-land-cover-from-aerospace","name":"FLAIR (French Land cover from Aerospace ImageRy)","full_name":null,"description_markdown":"The French National Institute of Geographical and Forest Information (IGN) has the mission to document and measure land-cover on French territory and provides referential geographical datasets, including high-resolution aerial images and topographic maps. The monitoring of land-cover plays a crucial role in land management and planning initiatives, which can have significant socio-economic and environmental impact. Together with remote sensing technologies, artificial intelligence (IA) promises to become a powerful tool in determining land-cover and its evolution. IGN is currently exploring the potential of IA in the production of high-resolution land cover maps. Notably, deep learning methods are employed to obtain a semantic segmentation of aerial images. However, territories as large as France imply heterogeneous contexts: variations in landscapes and image acquisition make it challenging to provide uniform, reliable and accurate results across all of France. \r\n\r\nThe FLAIR-one dataset presented is part of the dataset currently used at IGN to establish the French national reference land cover map \"Occupation du sol \\`a grande \\'echelle\" (OCS- GE).\r\nIt covers 810 km² and has 13 semantic classes.","description_withheld":null,"homepage":"https://ignf.github.io/FLAIR/","introduced_date":"2022-11-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/flair-1-semantic-segmentation-and-domain","title":"FLAIR #1: semantic segmentation and domain adaptation dataset","first_author":"Anatol Garioud","url":null},"license":{"name":"Licence Ouverte Etalab","url":"https://www.etalab.gouv.fr/licence-ouverte-open-licence/"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"}],"languages":[],"variants":["FLAIR (French Land cover from Aerospace ImageRy)"],"data_loaders":[{"repo":"https://github.com/IGNF/FLAIR-1-AI-Challenge","url":"https://github.com/IGNF/FLAIR-1-AI-Challenge","frameworks":[]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-flair-french-land","task":"Semantic Segmentation","dataset_variant":"FLAIR (French Land cover from Aerospace ImageRy)","rows":5,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"Ensemble-04 MiT-0 MiT-1 RNX-1 RNX-2","paper":"/paper/modernized-training-of-u-net-for-aerial","metrics":{"mIoU":"64.1"},"code_links":[{"title":"strakaj/U-Net-for-remote-sensing","url":"https://github.com/strakaj/U-Net-for-remote-sensing"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deep-multimodal-fusion-for-semantic","title":"Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data","date":"2024-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/modernized-training-of-u-net-for-aerial","title":"Modernized Training of U-Net for Aerial Semantic Segmentation","date":"2024-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/flair-a-country-scale-land-cover-semantic","title":"FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery","date":"2023-10-20","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":6,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flair-1-semantic-segmentation-and-domain","title":"FLAIR #1: semantic segmentation and domain adaptation dataset","date":"2022-11-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":9,"samples_ran":8,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":17,"samples_ran":14,"samples_unverified":3,"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."}