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

TL;DR

This paper introduces a deep learning-based method using semantic segmentation to quantify damage in urban environments after natural disasters, providing a numerical coefficient of change for damage assessment.

Contribution

It develops a novel automated workflow combining aerial imagery and CNN segmentation to measure damage, offering a new quantitative damage index.

Findings

Seg-Net achieved 92% segmentation accuracy.

The method generated a 10,000-image database for analysis.

The damage coefficient correlates with disaster impact.

Abstract

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.