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

Understand

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.