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AI for Good
book

AI for Good

by Juan M. Lavista Ferres, William B. Weeks, Brad Smith
April 2024
Intermediate to advanced content levelIntermediate to advanced
432 pages
9h 44m
English
Wiley
Audiobook available
Content preview from AI for Good

Chapter 15Dwelling Type Classification

—Md Nasir and Anshu Sharma

Executive Summary

Neighborhood vulnerability and risk assessment is essential for effective disaster preparedness. Due to their dependency on time-consuming and cost-intensive field surveying, existing systems of neighborhood vulnerability and risk assessment do not provide a scalable way to assess the precise extent of natural-hazard risk at a hyper-local level. Here, we used machine learning to automate the process of identifying dwellings and their type, allowing us to build a potentially more effective and expansive disaster vulnerability assessment system.

First, we used satellite images of low-income settlements and vulnerable areas in India to identify seven different dwelling types. Specifically, we formulated the dwelling type classification as a semantic segmentation task (identifying and classifying discrete dwellings and their types) and trained a U-Net–based neural network model with the data we collected. Then, we used a risk score assessment model that incorporated the determined dwelling type along with a flood inundation model of the regions. We used 2020 data obtained prior to natural hazards that occurred in India thereafter. We collected post-disaster ground-truth data from those regions to validate the efficacy of our model. We found that our model performed well. This work can drive preemptive action by providing household-level risk indicators that can inform decision-making by disaster ...

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Publisher Resources

ISBN: 9781394235872Purchase Link