{"url":"/dataset/worldfloods","name":"WorldFloods","full_name":null,"description_markdown":"WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations","description_withheld":null,"homepage":"https://www.nature.com/articles/s41598-021-86650-z","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"}],"languages":[],"variants":["WorldFloods"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-semantic-segmentation-on-worldfloods","task":"2D Semantic Segmentation","dataset_variant":"WorldFloods","rows":1,"metrics":["Category mIoU","GMac","MParams"],"first_row_in_archive_order":{"model":"SWRNet","paper":"/paper/swrnet-a-deep-learning-approach-for-small","metrics":{"Category mIoU":"0.789","GMac":"10.69","MParams":"1.95"},"code_links":[{"title":"trongan93/swrnet","url":"https://github.com/trongan93/swrnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/swrnet-a-deep-learning-approach-for-small","title":"SWRNet: A Deep Learning Approach for Small Surface Water Area Recognition Onboard Satellite","date":"2023-10-27","rows_on_this_dataset":1,"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."}