Dwelling Type Classification for Disaster Risk Assessment Using Satellite Imagery

TL;DR

This paper presents a machine learning approach using satellite imagery and semantic segmentation to classify dwelling types in vulnerable areas, enabling more precise disaster risk assessment at a household level.

Contribution

It introduces a novel pipeline combining dwelling classification with risk scoring, validated on real-world data in India, improving disaster vulnerability assessment accuracy.

Findings

High accuracy in dwelling type classification

Effective risk scoring based on dwelling types and inundation models

Deployment in multiple locations demonstrated practical utility

Abstract

Vulnerability and risk assessment of neighborhoods is essential for effective disaster preparedness. Existing traditional systems, due to dependency on time-consuming and cost-intensive field surveying, do not provide a scalable way to decipher warnings and assess the precise extent of the risk at a hyper-local level. In this work, machine learning was used to automate the process of identifying dwellings and their type to build a potentially more effective disaster vulnerability assessment system. First, satellite imageries of low-income settlements and vulnerable areas in India were used to identify 7 different dwelling types. Specifically, we formulated the dwelling type classification as a semantic segmentation task and trained a U-net based neural network model, namely TernausNet, with the data we collected. Then a risk score assessment model was employed, using the determined dwelling type along with an inundation model of the regions. The entire pipeline was deployed to multiple locations prior to natural hazards in India in 2020. Post hoc ground-truth data from those regions was collected to validate the efficacy of this model which showed promising performance. This work can aid disaster response organizations and communities at risk by providing household-level risk information that can inform preemptive actions.