{"url":"/dataset/munich-sentinel2-crop-segmentation","name":"Munich Sentinel2 Crop Segmentation","full_name":null,"description_markdown":"Contains squared blocks of 48×48 pixels including 13 Sentinel-2 bands. \r\nEach 480-m block was mined from a large geographical area of interest (102 km × 42 km) located north of Munich, Germany.","description_withheld":null,"homepage":"https://zenodo.org/records/5712933","introduced_date":"2018-02-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-temporal-land-cover-classification-with","title":"Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders","first_author":"Marc Rußwurm","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"UNET Segmentation","url":"/task/unet-segmentation","datasets_with_task":"/datasets/task/unet-segmentation"}],"languages":[],"variants":["Munich Sentinel2 Crop Segmentation"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unet-segmentation-on-munich-sentinel2-crop-1","task":"UNET Segmentation","dataset_variant":"Munich Sentinel2 Crop Segmentation","rows":5,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"Swin UNETR","paper":"/paper/enhancing-crop-segmentation-in-satellite","metrics":{"Overall Accuracy":"95.26"},"code_links":[{"title":"mattiagatti/sentinel2-crop-mapping-models","url":"https://gitlab.com/mattiagatti/sentinel2-crop-mapping-models"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/enhancing-crop-segmentation-in-satellite","title":"Enhancing crop segmentation in satellite image time-series with transformer networks","date":"2024-04-03","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/sentinel-2-time-series-analysis-with-3d","title":"Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping","date":"2021-10-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-temporal-land-cover-classification-with","title":"Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders","date":"2018-02-06","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."}