{"url":"/dataset/xbd","name":"xBD","full_name":null,"description_markdown":"The xBD dataset contains over 45,000KM2 of polygon labeled pre and post disaster imagery. The dataset provides the post-disaster imagery with transposed polygons from pre over the buildings, with damage classification labels.\r\n\r\nSource: [xBD](https://github.com/DIUx-xView/xview2-baseline)\r\nImage Source: [Gupta et al](https://arxiv.org/pdf/1911.09296.pdf)","description_withheld":null,"homepage":"https://github.com/DIUx-xView/xview2-baseline","introduced_date":"2019-02-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/xbd-a-dataset-for-assessing-building-damage","title":"xBD: A Dataset for Assessing Building Damage from Satellite Imagery","first_author":"Ritwik Gupta","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"Data Augmentation","url":"/task/data-augmentation","datasets_with_task":"/datasets/task/data-augmentation"},{"name":"Extracting Buildings In Remote Sensing Images","url":"/task/extracting-buildings-in-remote-sensing-images","datasets_with_task":"/datasets/task/extracting-buildings-in-remote-sensing-images"},{"name":"Disaster Response","url":"/task/disaster-response","datasets_with_task":"/datasets/task/disaster-response"}],"languages":[],"variants":["xBD"],"data_loaders":[{"repo":"https://github.com/DIUx-xView/xview2-baseline","url":"https://github.com/DIUx-xView/xview2-baseline","frameworks":["tf"]}],"num_papers_in_archive":51,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/extracting-buildings-in-remote-sensing-images","task":"Extracting Buildings In Remote Sensing Images","dataset_variant":"xBD","rows":7,"metrics":["F1","IoU"],"first_row_in_archive_order":{"model":"SiamixFormer-5","paper":"/paper/siamixformer-a-siamese-transformer-network","metrics":{"F1":"88.43","IoU":"79.26"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/2d-semantic-segmentation-on-xbd","task":"2D Semantic Segmentation","dataset_variant":"xBD","rows":5,"metrics":["Weighted Average F1-score","Localization F1-score","Classification F1-score"],"first_row_in_archive_order":{"model":"MambaBDA-Base","paper":"/paper/changemamba-remote-sensing-change-detection","metrics":{"Classification F1-score":"0.7884","Localization F1-score":"0.8141","Weighted Average F1-score":"0.8141"},"code_links":[{"title":"chenhongruixuan/mambacd","url":"https://github.com/chenhongruixuan/mambacd"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/changemamba-remote-sensing-change-detection","title":"ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model","date":"2024-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/siamixformer-a-siamese-transformer-network","title":"SiamixFormer: a fully-transformer Siamese network with temporal Fusion for accurate building detection and change detection in bi-temporal remote sensing images","date":"2022-08-01","rows_on_this_dataset":6,"code_links":0,"syntology":null},{"paper":"/paper/dual-tasks-siamese-transformer-framework-for","title":"Dual-Tasks Siamese Transformer Framework for Building Damage Assessment","date":"2022-01-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/bdanet-multiscale-convolutional-neural","title":"BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images","date":"2021-05-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-directional-feature-fusion-network-for","title":"Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery","date":"2020-10-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/building-disaster-damage-assessment-in","title":"Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion","date":"2020-04-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/xbd-a-dataset-for-assessing-building-damage","title":"xBD: A Dataset for Assessing Building Damage from Satellite Imagery","date":"2019-11-21","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":11,"samples_ran":5,"samples_unverified":6,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":2,"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."}