{"url":"/task/flood-extent-forecasting","name":"Flood extent forecasting","slug":"flood-extent-forecasting","description_markdown":"Flood extent forecasting is the task of predicting a binary 2D flood extent map (water vs no water), given input drivers and forcings. The focus is specifically on the impact and extent modeling, such that e.g. atmosphere state (such as precipitation) may be assumed as inputs, to disentangle the impact modeling from upstream challenges such as weather forecasting. This is complementary to time series forecasting of river streamflow and runoff, as well as post-hoc mapping of floods. For related work, data & benchmarks, see https://arxiv.org/abs/2409.18591.","categories":[{"name":"Computer Code","url":"/area/computer-code"},{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Medical","url":"/area/medical"},{"name":"Robots","url":"/area/robots"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":5,"papers_with_code":5,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":1,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/flood-extent-forecasting-on-global-flood","slug":"flood-extent-forecasting-on-global-flood","dataset":"Global Flood forecasting","dataset_url":"/dataset/gff","rows_in_archive":5,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"U-TAE","paper_title":"Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks","paper_url":"/paper/panoptic-segmentation-of-satellite-image-time","paper_date":"2021-07-16","arxiv_id":"2107.07933","code_links":[{"title":"VSainteuf/utae-paps","url":"https://github.com/VSainteuf/utae-paps"}],"syntology":{"n":11,"n_ran":10,"n_unverified":1,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/gff","name":"GFF","full_name":"Global Flood Forecasting","num_papers_in_archive":6}],"subtasks":[],"parent_tasks":[{"url":"/task/semantic-segmentation","name":"Semantic Segmentation"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":5,"of":5,"tagged_in_all":5,"items":[{"url":"/paper/maxvit-unet-multi-axis-attention-for-medical","title":"MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation","date":"2023-05-15","arxiv_id":"2305.08396","repositories_listed":2,"syntology":null},{"url":"/paper/off-to-new-shores-a-dataset-benchmark-for","title":"Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting","date":"2024-09-27","arxiv_id":"2409.18591","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_unverified":4,"n_pointer_only":0}},{"url":"/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","arxiv_id":"2112.02447","repositories_listed":1,"syntology":{"n":15,"n_ran":0,"n_unverified":15,"n_pointer_only":0}},{"url":"/paper/panoptic-segmentation-of-satellite-image-time","title":"Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks","date":"2021-07-16","arxiv_id":"2107.07933","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_unverified":1,"n_pointer_only":0}},{"url":"/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","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}