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Good practice Imported

SKAI — WFP and Google Research's Open-Source AI for Rapid Earthquake Damage Mapping in Türkiye

Türkiye · Kahramanmaraş · See the Türkiye profile

Evidence: Descriptive / self-reported Top 7% 87/100 · Ask Evidence Copilot about this practice

After the February 2023 earthquake, a 5-person WFP/Google Research team used the open-source SKAI tool to assess ~600,000 buildings across southeast Türkiye with 81%+ accuracy in under a month, flagging 28,000+ destroyed structures — impact not independently verified.

~600,000 buildings
Buildings assessed in southeast Türkiye (within one month, Feb 2023)
>81%
Damage-mapping accuracy
28,000+
Heavily damaged or destroyed structures flagged
265,000+ buildings
First-pass buildings assessed across three cities (within one week)
2.1 million+
Cumulative buildings assessed globally since 2022 release
88%
Durban floods precision (South Africa deployment)

Details

Maturity
Scaling
Promoter
World Food Programme (WFP) Innovation Accelerator & Google Research
Period
2022-2023
Keywords
disaster response, emergency management, satellite imagery, machine learning, humanitarian aid

Context

SKAI is an open-source machine-learning tool built by the World Food Programme's Innovation Accelerator with Google Research to speed up post-disaster building-damage assessment from satellite and aerial imagery. Released as open source in June 2022, it was designed to be context-agnostic, usable across earthquake, flood, hurricane and cyclone responses without retraining from scratch for each disaster. It was put to its highest-profile test after the February 2023 Türkiye-Syria earthquake.

Objectives

The tool aims to give humanitarian and disaster-response agencies a fast, scalable way to identify which buildings have been destroyed or heavily damaged, so that response resources can be routed to where they are needed most, without waiting for slow manual assessment.

Activities

A five-person joint WFP/Google Research team assessed roughly 600,000 buildings across southeast Türkiye in under a month, including a first pass of over 265,000 buildings across three major cities within a week, covering an area affected by roughly 5 million people. The output was shared to support the response coordinated by Türkiye's national disaster agency AFAD, alongside separate satellite-mapping work by Microsoft's AI for Good Lab.

Results

The Türkiye deployment reached over 81% accuracy and flagged more than 28,000 heavily damaged or destroyed structures. WFP has since redeployed SKAI for Hurricane Ian in the United States (410,000 buildings, ~70% precision/recall), the 2022 Pakistan floods (850,000 households, 80–85% precision) and flooding in Durban, South Africa (250,000 buildings, 88% precision), with the tool assessing more than 2.1 million buildings cumulatively since release.

Conclusions

WFP's own account of the Türkiye deployment is candid about its limits, noting that the real-world impact of the AI-generated damage maps was difficult to measure and not intensively verified against on-the-ground outcomes. SKAI is a genuine, repeatedly reused open-source deployment, but its downstream humanitarian impact has not been independently evaluated.

Implementation

Indicative cost
Low (< €50k)
Time to results
Short (< 1 year)
Staffing & skills
Five-person joint WFP Innovation Accelerator / Google Research team

Conditions for success

  • Pre-existing open-source, context-agnostic model requiring no per-disaster retraining
  • Partnership with a national disaster agency (Türkiye's AFAD) to route outputs into the response
  • Rapid availability of satellite and aerial imagery shortly after the disaster

Common failure modes

  • Real-world impact of the damage maps was not intensively verified against ground truth

Commonly funded by

National / regional programmes Philanthropic / foundation funding

Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.

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Data sources

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