  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 coefficient of change
represents the damage numerically an urban environment had to quantify the
overall damage in the built environment. Such an index can give the government
an estimate of the number of affected households and perhaps the extent of
housing damage.
