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Workshop: Third Workshop on AI for Humanitarian Assistance and Disaster Response

Creating a Coefficient of Change in the Built Environment After a Natural Disaster

Karla Saldana Ochoa


This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such a coefficient of change quantifies the overall damage in the built environment; such information gives an estimate of the number of affected households and perhaps the extent of housing damage.