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Machine Learning-based Damage Assessment for Disaster Relief

Tuesday, June 16, 2020

Posted by Joseph Xu, Senior Software Engineer and Pranav Khaitan, Engineering Lead, Google ResearchBuilding Damage Detection in Satellite Imagery Using Convolutional Neural NetworksUnited Nations World Food Program (WFP) Innovation AcceleratorThe Approach

convolutional neural networkhistogram equalizationTraining DataUNOSATREACHGoogle Earth Engine

Examples of individual image patches that capture before and after images of damaged and undamaged buildings from different disasters.

ResultsROCtrue positive and false positive rates

Area under the

Event

Accuracy

ROC curve

2010 Haiti earthquake

77%

0.83

2017 Mexico City earthquake

71%

0.79

2018 Indonesia earthquake

78%

0.86

Evaluation of model predictions against human expert assessments (higher is better).

Example model predictions from the 2010 Haiti earthquake. Prediction values closer to 1.0 means the model is more confident that the building is damaged. Values closer to 0.0 means the building is not damaged. A threshold value of 0.5 is typically used to distinguish between damaged/undamaged predictions, but this can be tuned to make the predictions more or less sensitive.

Future WorkAcknowledgementsThis post reflects the work of our co-authors Wenhan Lu and Zebo Li. We would also like to thank Maolin Zuo for his contributions to the project. In tackling this problem, we have had a very productive partnership with the United Nations World Food Programme (WFP) Innovation Accelerator, an organization that identifies, funds, and supports startups and innovative projects to disrupt world hunger.