{"url":"/dataset/mila-simulated-floods","name":"Mila Simulated Floods","full_name":null,"description_markdown":"Mila Simulated Floods Dataset is a 1.5 square km virtual world using the Unity3D game engine including urban, suburban and rural areas.\r\n\r\nThe *urban* environment contains skyscrapers, large buildings, and roads, as well as objects such as traffic items and vehicles. The *rural* environment consists of a landscape of grassy hills, forests, and mountains, with sparse houses and other buildings such as a church, and no roads. The rural and urban areas make up for 1 square km of our virtual world. \r\nThe *suburban* environment is a residential area of 0.5 square km with many individual houses with front yards.\r\n\r\nTo gather the simulated dataset, we captured *before* and *after* flood pairs from 2000 viewpoints with the following modalities:\r\n\r\n- non-flooded RGB image, depth map, segmentation map\r\n- flooded RGB image, binary mask of the flooded area, segmentation map\r\n\r\nThe camera was placed about 1.5 above ground, and has a field of view of *120 degree*, and the resolution of the images is *1200 x 900*. At each viewpoint, we took 10 pictures, by varying slightly the position of the camera in order to augment the dataset.","description_withheld":null,"homepage":"https://github.com/cc-ai/mila-simulated-floods","introduced_date":"2021-10-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/climategan-raising-climate-change-awareness","title":"ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods","first_author":"Victor Schmidt","url":null},"license":{"name":"CC-BY","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Image-to-Image Translation","url":"/task/image-to-image-translation","datasets_with_task":"/datasets/task/image-to-image-translation"},{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Scene Segmentation","url":"/task/scene-segmentation","datasets_with_task":"/datasets/task/scene-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Mila Simulated Floods"],"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-mila-simulated","task":"Semantic Segmentation","dataset_variant":"Mila Simulated Floods","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"FloodTransformer (Ours)","paper":"/paper/transformer-based-flood-scene-segmentation","metrics":{"mIoU":"0.93"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/transformer-based-flood-scene-segmentation","title":"Transformer-based Flood Scene Segmentation for Developing Countries","date":"2022-10-09","rows_on_this_dataset":1,"code_links":0,"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."}