The SKAI isn’t the limit: How WFP uses satellite imagery and machine learning in emergencies

Overview of SKAI
SKAI is unique in having been trained with past-onset disasters that were previously tagged by manual analysts.
In the aftermath of a disaster, access to information is critical to allocate resources and reach people in need of assistance. In the photo: aerial image of Beira, Mozambique, after Cyclone Idai in 2019. Photo: WFP/INGC/Antonio Jose Beleza

Introducing SKAI

WFP partnered with Google Research to set up SKAI, a humanitarian response mapping project powered by artificial intelligence — an approach that combines statistical methods, data and modern computing techniques to automate specific tasks. SKAI assesses damage to buildings by applying computer vision — computer algorithms that can interpret information extracted from visual materials such as, in this case, satellite images of areas impacted by conflict, climate events, or other disasters. The key to this process is a machine learning model developed specifically for SKAI.

Approach: machine learning on satellite imagery

SKAI aims to provide building damage assessment at scale within 24 hours after obtaining clear, high-quality satellite imagery following a disaster. SKAI’s machine learning model detects damaged buildings by comparing imagery of the same buildings before and after the disaster.

SKAI uses artificial intelligence to analyze satellite images to automatically assess damage post disasters.

Lessons learned from SKAI deployment in humanitarian operations

WFP trained SKAI during more recent disasters, including the Beirut Port blast in Lebanon in 2020 and the Cyclone Yasa in Fiji in 2021. We have learned that SKAI requires context specific setup and calibration, depending on the type of disaster and the size of the disaster area footprint.

The destruction at Beirut’s port following a huge chemical explosion that devastated large parts of the Lebanese capital in 2020. Photo: WFP/Malak Jaafar

The way forward

SKAI serves as an example of how leading edge technologies and partnerships with the tech sector can optimize monitoring, planning, decision making and overall effectiveness of emergency response after disasters, while safeguarding humanitarian principles.

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