{"url":"/dataset/lombardia-sentinel-2-image-time-series-for","name":"Lombardia Sentinel-2 Image Time Series for Crop Mapping","full_name":null,"description_markdown":"Usually, the information related to the crop types available in a given territory is annual information, that is, we only know the type of main crop grown over a year and we do not know any crops that have followed one another during the year and also we do not know when a particular crop is sown and when it is harvested.\r\nThe main objective of this dataset is to create the basis for experimenting with suitable solutions to give a reliable answer to the above questions, or to propose models capable of producing dynamic segmentation maps that show when a crop begins to grow and when it is collected. Consequently, being able to understand if more than one crop has been grown in a territory within a year.\r\nIn this dataset, we have 20 coverage classes as ground-truth values provided by Regine Lombardia.\r\nThe mapping of the class labels used (see file lombardia-classes/classes25pc.txt) brings together some classes and provides the time intervals within which that category grows.\r\nThe last two columns of the following table are respectively the date (month-day) of the start and end of the interval in which the class is visible during the construction of our dataset.","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/ignazio/sentinel2-crop-mapping","introduced_date":"2022-12-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/in-season-and-dynamic-crop-mapping-using-3d","title":"In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series","first_author":"Ignazio Gallo","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["Lombardia Sentinel-2 Image Time Series for Crop Mapping"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-lombardia-sentinel-2","task":"Semantic Segmentation","dataset_variant":"Lombardia Sentinel-2 Image Time Series for Crop Mapping","rows":4,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"UNet3D","paper":"/paper/enhancing-crop-segmentation-in-satellite","metrics":{"Overall Accuracy":"80.77"},"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":4,"code_links":1,"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."}