{ "id": "AIBD-0346", "title": "xView2 machine-learning damage maps may have been used by UN INSARAG teams in Adıyaman and by AFAD, the World Bank, IFRC and WFP after the 6 February 2023 Türkiye earthquakes", "summary": "MIT Technology Review (20 February 2023), quoting DIU: xView2 used by two UN INSARAG teams in Adıyaman and by AFAD, World Bank, IFRC and WFP; Microsoft reports 3,849 damaged buildings mapped for AFAD.", "date_observed": "2023-02-20", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources.", "category": "protective response", "unexpected": false, "purpose": "Rapid building-damage assessment from pre- and post-event satellite imagery for humanitarian assistance and disaster response. This use was that purpose.", "system": "xView2 building damage assessment models (semantic segmentation on satellite imagery, trained on the xBD dataset)", "model_version": null, "developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley", "deployer": "UN INSARAG (search and rescue ground teams in Adıyaman); AFAD, World Bank, IFRC and WFP (per the developer's account); Microsoft AI for Good delivered estimates to AFAD (per Microsoft)", "location": "Adıyaman and four cities in southeast Türkiye including Kahramanmaraş (Marash)", "domain": "Disaster response / post-earthquake damage assessment", "segment": "Humanitarian and civil-protection organizations", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": "The responder-use claim rests on the developer's account relayed by one news item and Microsoft's own report.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "No individual rescue or beneficiary is attributed to the maps in any cited source." }, { "group": "organizational", "depth": 2, "reason": "Responders are reported to have received building-level damage maps in hours instead of weeks and to have found damaged areas they were unaware of. The reports come from the developer and a World Bank specialist, not the responding agencies." }, { "group": "societal", "depth": 1, "reason": "No downstream effect on rescues, shelter siting or reconstruction is documented. The maps covered part of the affected zone after imagery became usable on 9 February." } ], "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. Which decisions changed is not documented. Sources: the developer (DIU's Gupta) and the World Bank specialist, quoted in one news report, and Microsoft's own report of delivering estimates to AFAD. World Bank's GRADE report and DLR's account do not corroborate operational use of AI products." }, "evidence": [ { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). How AI can actually be helpful in disaster response. MIT Technology Review, 20 Feb 2023.", "title": "How AI can actually be helpful in disaster response", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "xView2 has been used by at least two different ground teams of search and rescue personnel from the UN’s International Search and Rescue Advisory Group in Adiyaman" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, World Bank specialist on assessment speed.", "title": "How AI can actually be helpful in disaster response (World Bank)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes." }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, method description.", "title": "How AI can actually be helpful in disaster response (method)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity" }, { "kind": "report", "citation": "Robinson, C., Gupta, R., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., Lavista Ferres, J. M. (2023). Turkey Earthquake Report. Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "date": null, "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "date": null, "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ], "replication": { "produced_by": [ "Usable high-resolution post-event satellite imagery (first available 9 February 2023, three days after the quake)", "A pre-trained open-source damage model (xBD/xView2) run by the developers on request of responding organizations", "Direct contact between DIU/Microsoft researchers and responders (UN INSARAG teams, AFAD)" ], "must_hold": [ "Cloud-free daytime imagery with a satellite overhead. The article notes far fewer images in remote or less developed areas such as Syria", "Responder trust in an off-site AI product, which the developer describes as hard to obtain", "Documentation by the responding agency of what the maps changed, which no cited source provides" ], "echoes": [ { "date": null, "setting": "Earlier deployments by the California National Guard and the Australian Geospatial-Intelligence Organization for wildfires and after flooding in Nepal, per the same MIT Technology Review article; no outcomes documented", "model_version": null, "outcome": "unknown", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "date": "2025-12-10", "setting": "Research study by Direct Relief and Stanford assessing health-facility damage with Microsoft AI for Good and Google models for the 2023 earthquakes (not an operational use)", "model_version": null, "outcome": "unknown", "source": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/" } ] }, "paired_incident": null, "headline": "UN INSARAG teams in Adıyaman may have used xView2 damage maps", "metric": "estimating 3,849 buildings damaged or destroyed", "comparator": { "against": "conventional damage assessment", "before": "weeks", "after": "hours or minutes", "basis": "assumed", "quote": "this same degree of assessment would typically take weeks and now takes hours or minutes", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, "later": { "status": "unknown", "date": null, "note": "Direct Relief's 10 December 2025 article describes a retrospective research study of the Microsoft and Google 2023 models that found they disagreed in significant ways; no later operational use of xView2 was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "United States", "deployer_country": "Switzerland", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Headline reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Knowledge Discovery under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Defense_Innovation_Unit; https://en.wikipedia.org/wiki/International_Search_and_Rescue_Advisory_Group", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to UN INSARAG.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports, the makers' report and the makers' preprint. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0346", "catalog_version": "1.0.0", "source_fingerprint": "0552f28b520cecd7eb1ac0a7189af9fb84ad3f2ef360d27b88bb995479c5f0d7", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0346-source-0001", "AIBD-0346-source-0002", "AIBD-0346-source-0003", "AIBD-0346-source-0004", "AIBD-0346-source-0005", "AIBD-0346-source-0006", "AIBD-0346-source-0007", "AIBD-0346-source-0008" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley", "deployer": "UN INSARAG (search and rescue ground teams in Adıyaman); AFAD, World Bank, IFRC and WFP (per the developer's account); Microsoft AI for Good delivered estimates to AFAD (per Microsoft)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The AI produced the damage classifications that responders are said to have used. 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It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. 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The reports come from the developer and a World Bank specialist, not the responding agencies." }, { "group": "societal", "depth": 1, "reason": "No downstream effect on rescues, shelter siting or reconstruction is documented. The maps covered part of the affected zone after imagery became usable on 9 February." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Rapid building-damage assessment from pre- and post-event satellite imagery for humanitarian assistance and disaster response. 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The article notes far fewer images in remote or less developed areas such as Syria", "Responder trust in an off-site AI product, which the developer describes as hard to obtain", "Documentation by the responding agency of what the maps changed, which no cited source provides" ], "echoes": [ { "setting": "Earlier deployments by the California National Guard and the Australian Geospatial-Intelligence Organization for wildfires and after flooding in Nepal, per the same MIT Technology Review article; no outcomes documented", "outcome": "unknown", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "date": "2025-12-10", "setting": "Research study by Direct Relief and Stanford assessing health-facility damage with Microsoft AI for Good and Google models for the 2023 earthquakes (not an operational use)", "outcome": "unknown", "source": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Usable high-resolution post-event satellite imagery (first available 9 February 2023, three days after the quake)", "A pre-trained open-source damage model (xBD/xView2) run by the developers on request of responding organizations", "Direct contact between DIU/Microsoft researchers and responders (UN INSARAG teams, AFAD)" ], "must_hold": [ "Cloud-free daytime imagery with a satellite overhead. 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Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). 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Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0346-source-0001", "AIBD-0346-source-0002", "AIBD-0346-source-0003", "AIBD-0346-source-0004", "AIBD-0346-source-0005", "AIBD-0346-source-0006", "AIBD-0346-source-0007", "AIBD-0346-source-0008" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). How AI can actually be helpful in disaster response. MIT Technology Review, 20 Feb 2023.", "title": "How AI can actually be helpful in disaster response", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "xView2 has been used by at least two different ground teams of search and rescue personnel from the UN’s International Search and Rescue Advisory Group in Adiyaman" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, organisations named by DIU's Ritwik Gupta.", "title": "How AI can actually be helpful in disaster response (organisations)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "noting Turkey’s Disaster and Emergency Management Presidency, the World Bank, the International Federation of the Red Cross, and the United Nations World Food Programme have all used the platform" }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, World Bank specialist on assessment speed.", "title": "How AI can actually be helpful in disaster response (World Bank)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "Atishay Abbhi, a disaster risk management specialist at the World Bank, tells me that this same degree of assessment would typically take weeks and now takes hours or minutes." }, { "kind": "news report", "citation": "Ryan-Mosley, T. (2023). Same article, method description.", "title": "How AI can actually be helpful in disaster response (method)", "url": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/", "date": "2023-02-20", "written_by": "independent", "quote": "It uses machine-learning algorithms in conjunction with satellite imagery from other providers to identify building and infrastructure damage in the disaster area and categorize its severity" }, { "kind": "report", "citation": "Robinson, C., Gupta, R., Fobi Nsutezo, S., Pound, E., Ortiz, A., Rosa, M., White, K., Dodhia, R., Zolli, A., Birge, C., Lavista Ferres, J. M. (2023). Turkey Earthquake Report. Microsoft AI for Good Research Lab, February 2023.", "title": "Turkey Earthquake Report", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "partnered with Turkey's Ministry of Interior Disaster and Emergency Management Presidency (AFAD) to deliver building level damage estimates over four cities in southeast Turkey" }, { "kind": "report", "citation": "Robinson, C. et al. (2023). Same report, results.", "title": "Turkey Earthquake Report (results)", "url": "https://www.microsoft.com/en-us/research/publication/turkey-earthquake-report/", "written_by": "makers", "quote": "3,849 buildings were damaged/destroyed across the four cities" }, { "kind": "preprint", "citation": "Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv:1911.09296.", "title": "xBD: A Dataset for Assessing Building Damage from Satellite Imagery", "url": "https://arxiv.org/abs/1911.09296", "date": "2019-11-21", "written_by": "makers", "quote": "Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life." }, { "kind": "news report", "citation": "Direct Relief (2025). After Disaster, AI Models Support Rapid Understanding of Health Facility Damage. 10 Dec 2025 (describes a Direct Relief/Stanford research study in PLOS Digital Health; does not mention xView2).", "title": "After Disaster, AI Models Support Rapid Understanding of Health Facility Damage", "url": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/", "date": "2025-12-10", "written_by": "independent", "quote": "Two of the most prominent models in use during the 2023 earthquake response came from Microsoft AI for Good and from Google." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "MIT Technology Review (20 February 2023), quoting DIU: xView2 used by two UN INSARAG teams in Adıyaman and by AFAD, World Bank, IFRC and WFP; Microsoft reports 3,849 damaged buildings mapped for AFAD.", "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "xView2 building damage assessment models (semantic segmentation on satellite imagery, trained on the xBD dataset)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "US Defense Innovation Unit (DIU) with Carnegie Mellon University Software Engineering Institute; research partners Microsoft AI for Good and UC Berkeley" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "xView2 machine-learning damage maps may have been used by UN INSARAG teams in Adıyaman and by AFAD, the World Bank, IFRC and WFP after the 6 February 2023 Türkiye earthquakes", "headline": "UN INSARAG teams in Adıyaman may have used xView2 damage maps" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "0552f28b520cecd7eb1ac0a7189af9fb84ad3f2ef360d27b88bb995479c5f0d7" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "MIT Technology Review reported on 20 February 2023 on xView2, an open-source building-damage assessment project. It was sponsored and developed in 2019 by the Pentagon's Defense Innovation Unit (DIU) and Carnegie Mellon University's Software Engineering Institute. Partners included Microsoft and UC Berkeley. It uses machine-learning (semantic segmentation) on satellite imagery to identify and grade building damage. Ritwik Gupta, DIU's principal AI scientist, told the magazine about use in Türkiye. At least two ground teams of search and rescue personnel from the UN's International Search and Rescue Advisory Group (INSARAG) had used xView2 in Adıyaman. He said it helped workers find damaged areas they were unaware of. He said AFAD, the World Bank, the IFRC and WFP had all used the platform. Atishay Abbhi, a World Bank disaster risk management specialist, said the same degree of assessment would typically take weeks and now takes hours or minutes. The article notes that the first usable satellite images came on 9 February 2023, three days after the first quake. It says the model is up to 85 or 90% accurate. Microsoft's February 2023 “Turkey Earthquake Report” has authors including Gupta and Microsoft AI for Good staff. It states that the team partnered with AFAD to deliver building-level damage estimates over four cities in southeast Türkiye. The imagery was from the first three days. It estimated 3,849 buildings damaged or destroyed, with Marash the most affected at 7.44% of buildings. The World Bank GRADE report does not mention xView2, AI or Microsoft. DLR's peer-reviewed account states its own AI damage products were not part of the viewer delivered to responders. Direct Relief's December 2025 article describes a research study on Microsoft and Google models rather than a response decision. No responder statement describing a decision taken on xView2 output is among the sources.", "replication": { "produced_by": [ "Usable high-resolution post-event satellite imagery (first available 9 February 2023, three days after the quake)", "A pre-trained open-source damage model (xBD/xView2) run by the developers on request of responding organizations", "Direct contact between DIU/Microsoft researchers and responders (UN INSARAG teams, AFAD)" ], "must_hold": [ "Cloud-free daytime imagery with a satellite overhead. The article notes far fewer images in remote or less developed areas such as Syria", "Responder trust in an off-site AI product, which the developer describes as hard to obtain", "Documentation by the responding agency of what the maps changed, which no cited source provides" ], "echoes": [ { "setting": "Earlier deployments by the California National Guard and the Australian Geospatial-Intelligence Organization for wildfires and after flooding in Nepal, per the same MIT Technology Review article; no outcomes documented", "outcome": "unknown", "source": "https://www.technologyreview.com/2023/02/20/1068824/ai-actually-helpful-disaster-response-turkey-syria-earthquake/" }, { "date": "2025-12-10", "setting": "Research study by Direct Relief and Stanford assessing health-facility damage with Microsoft AI for Good and Google models for the 2023 earthquakes (not an operational use)", "outcome": "unknown", "source": "https://www.directrelief.org/2025/12/after-disaster-ai-models-support-rapid-understanding-of-health-facility-damage/" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 29, "reviewed": 0, "record_mapped": 29, "not_assessed": 62, "complete": false, "by_state": { "value_present": 29, "not_reported": 0, "unresolved": 0, "not_assessed": 62, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }