{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/xbd-a-dataset-for-assessing-building-damage","title":"xBD: A Dataset for Assessing Building Damage from Satellite Imagery","arxiv_id":"1911.09296","date":"2019-11-21","proceeding":null,"authors":["Ritwik Gupta","Richard Hosfelt","Sandra Sajeev","Nirav Patel","Bryce Goodman","Jigar Doshi","Eric Heim","Howie Choset","Matthew Gaston"],"abstract":"We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD provides pre- and post-event satellite imagery across a variety of disaster events with building polygons, ordinal labels of damage level, and corresponding satellite metadata. Furthermore, the dataset contains bounding boxes and labels for environmental factors such as fire, water, and smoke. xBD is the largest building damage assessment dataset to date, containing 850,736 building annotations across 45,362 km\\textsuperscript{2} of imagery.","url_abs":"https://arxiv.org/abs/1911.09296v1","url_pdf":"https://arxiv.org/pdf/1911.09296v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"xbd-a-dataset-for-assessing-building-damage","repo_url":"https://github.com/DIUx-xView/xview2-baseline","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"xbd-a-dataset-for-assessing-building-damage","repo_url":"https://github.com/aleksispi/airloc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"xbd-a-dataset-for-assessing-building-damage","repo_url":"https://github.com/nimaafshar/metadamagenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"xbd-a-dataset-for-assessing-building-damage","repo_url":"https://github.com/yjt2018/awesome-remote-sensing-change-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"2d-semantic-segmentation","task_name":"2D Semantic Segmentation"},{"task_slug":"building-damage-assessment","task_name":"Building Damage Assessment"},{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"disaster-response","task_name":"Disaster Response"},{"task_slug":"humanitarian","task_name":"Humanitarian"}],"methods":[],"datasets_introduced":[{"slug":"xbd","name":"xBD","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/2d-semantic-segmentation-on-xbd","task":"2D Semantic Segmentation","dataset":"xBD","model":"Baseline Model","rank_in_archive_order":5,"of":5,"metrics":{"Weighted Average F1-score":"0.265"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.09296","atlas_url":"https://app.syntology.ai/?focus=1911.09296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09296"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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