{"url":"/dataset/bright","name":"BRIGHT","full_name":null,"description_markdown":"BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 14 disaster events in 23 regions worldwide, with a particular focus on developing countries.\r\n\r\nIt supports not only the development of supervised deep models, but also the testing of their performance on cross-event transfer setup, as well as unsupervised domain adaptation, semi-supervised learning, unsupervised change detection, and unsupervised image matching methods in multimodal and disaster scenarios.","description_withheld":null,"homepage":"https://github.com/ChenHongruixuan/BRIGHT","introduced_date":"2025-01-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/bright-a-globally-distributed-multimodal","title":"BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response","first_author":"Hongruixuan Chen","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"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":"Image Registration","url":"/task/image-registration","datasets_with_task":"/datasets/task/image-registration"},{"name":"Building Damage Assessment","url":"/task/building-damage-assessment","datasets_with_task":"/datasets/task/building-damage-assessment"},{"name":"Damaged Building Detection","url":"/task/damaged-building-detection","datasets_with_task":"/datasets/task/damaged-building-detection"}],"languages":[],"variants":["BRIGHT"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/building-damage-assessment-on-bright","task":"Building Damage Assessment","dataset_variant":"BRIGHT","rows":7,"metrics":["mIOU"],"first_row_in_archive_order":{"model":"ChangeMamba","paper":"/paper/bright-a-globally-distributed-multimodal","metrics":{"mIOU":"67.63"},"code_links":[{"title":"chenhongruixuan/bright","url":"https://github.com/chenhongruixuan/bright"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/bright-a-globally-distributed-multimodal","title":"BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response","date":"2025-01-10","rows_on_this_dataset":7,"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."}