{"url":"/dataset/gff","name":"GFF","full_name":"Global Flood Forecasting","description_markdown":"Floods are among the most common and devastating natural hazards, imposing immense costs on our society and economy due to their disastrous consequences.\r\nRecent progress in weather prediction and spaceborne flood mapping demonstrated the feasibility of anticipating extreme events and reliably detecting their catas-\r\ntrophic effects afterwards. However, these efforts are rarely linked to one another and there is a critical lack of datasets and benchmarks to enable the direct forecast-\r\ning of flood extent. To resolve this issue, we curate a novel dataset enabling a timely prediction of flood extent. Furthermore, we provide a representative evaluation\r\nof state-of-the-art methods, structured into two benchmark tracks for forecasting flood inundation maps i) in general and ii) focused on coastal regions. Altogether,\r\nour dataset and benchmark provide a comprehensive platform for evaluating flood forecasts, enabling future solutions for this critical challenge. Data, code & models\r\nare shared at https://github.com/Multihuntr/GFF under a CC0 license.","description_withheld":null,"homepage":"https://github.com/Multihuntr/gff","introduced_date":"2024-09-27","introduced_date_note":null,"introduced_by":{"paper":"/paper/off-to-new-shores-a-dataset-benchmark-for","title":"Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting","first_author":"Brandon Victor","url":null},"license":{"name":"CC0","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Change Detection","url":"/task/change-detection","datasets_with_task":"/datasets/task/change-detection"},{"name":"Weather Forecasting","url":"/task/weather-forecasting","datasets_with_task":"/datasets/task/weather-forecasting"},{"name":"Flood extent forecasting","url":"/task/flood-extent-forecasting","datasets_with_task":"/datasets/task/flood-extent-forecasting"}],"languages":[],"variants":["GFF","Global Flood forecasting"],"data_loaders":[{"repo":"https://github.com/multihuntr/gff","url":"https://github.com/multihuntr/gff","frameworks":["pytorch"]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/flood-extent-forecasting-on-global-flood","task":"Flood extent forecasting","dataset_variant":"Global Flood forecasting","rows":5,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"U-TAE","paper":"/paper/panoptic-segmentation-of-satellite-image-time","metrics":{"F1 score":"0.77"},"code_links":[{"title":"VSainteuf/utae-paps","url":"https://github.com/VSainteuf/utae-paps"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/maxvit-unet-multi-axis-attention-for-medical","title":"MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation","date":"2023-05-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/next-day-wildfire-spread-a-machine-learning","title":"Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data","date":"2021-12-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":0,"samples_unverified":15,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/panoptic-segmentation-of-satellite-image-time","title":"Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks","date":"2021-07-16","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":10,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semantic-segmentation-of-crop-type-in-africa","title":"Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods","date":"2019-06-01","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":26,"samples_ran":10,"samples_unverified":16,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}