{"url":"/dataset/landcover-ai","name":"LandCover.ai","full_name":"Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery","description_markdown":"The LandCover.ai (**Land Cover** from **A**erial **I**magery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images. \r\n\r\n### Dataset features\r\n\r\n* land cover from Poland, Central Europe\r\n* three spectral bands - RGB\r\n* 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px)\r\n* 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px)\r\n* total area of 216.27 sq. km\r\n\r\n### Dataset format\r\n\r\n* rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system\r\n* masks are single-channel GeoTiffs with EPSG:2180 spatial reference system","description_withheld":null,"homepage":"https://landcover.ai.linuxpolska.com/","introduced_date":"2020-05-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/landcover-ai-dataset-for-automatic-mapping-of","title":"LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery","first_author":"Adrian Boguszewski","url":null},"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Object Detection In Aerial Images","url":"/task/object-detection-in-aerial-images","datasets_with_task":"/datasets/task/object-detection-in-aerial-images"},{"name":"Bird's-Eye View Semantic Segmentation","url":"/task/bird-s-eye-view-semantic-segmentation","datasets_with_task":"/datasets/task/bird-s-eye-view-semantic-segmentation"},{"name":"Semantic Segmentation Of Orthoimagery","url":"/task/semantic-segmentation-of-orthoimagery","datasets_with_task":"/datasets/task/semantic-segmentation-of-orthoimagery"}],"languages":[],"variants":["LandCover.ai"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-landcover-ai","task":"Semantic Segmentation","dataset_variant":"LandCover.ai","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"U-Net (ConvFormer-M36)","paper":"/paper/u-net-ensemble-for-enhanced-semantic","metrics":{"mIoU":"87.64"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/u-net-ensemble-for-enhanced-semantic","title":"U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery","date":"2024-06-08","rows_on_this_dataset":1,"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."}