{"url":"/dataset/fine-grained-cloud-segmentation-dataset","name":"Fine-Grained Cloud Segmentation Dataset","full_name":null,"description_markdown":"The dataset consists of 96 terrain-corrected (Level-1T) scenes from Landsat 8 OLI and TIRS, covering diverse biomes. This variety supports cloud detection and removal in complex environments. The dataset includes manually generated cloud masks with pixel-level annotations for cloud shadow, clear sky, thin clouds, and cloud areas. Each scene is cropped into 512×512 pixel patches and split into training, validation, and test sets (6:2:2 ratio). It is a valuable resource for training and evaluating fine-grained cloud segmentation models across various terrains.","description_withheld":null,"homepage":"https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data","introduced_date":"2018-10-13","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["Fine-Grained Cloud Segmentation Dataset"],"data_loaders":[],"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-fine-grained-cloud","task":"Semantic Segmentation","dataset_variant":"Fine-Grained Cloud Segmentation Dataset","rows":4,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"D2LS","paper":"/paper/dynamic-dictionary-learning-for-remote","metrics":{"mIoU":"82.16"},"code_links":[{"title":"XavierJiezou/D2LS","url":"https://github.com/XavierJiezou/D2LS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dynamic-dictionary-learning-for-remote","title":"Dynamic Dictionary Learning for Remote Sensing Image Segmentation","date":"2025-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/knowledge-transfer-and-domain-adaptation-for","title":"Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation","date":"2024-12-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sfa-net-semantic-feature-adjustment-network","title":"SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation","date":"2024-09-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/high-resolution-cloud-detection-network","title":"High-Resolution Cloud Detection Network","date":"2024-07-10","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":12,"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."}