Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images

TL;DR

This paper presents a deep learning-based system using satellite imagery to quantify disaster damage in real-time, aiding relief efforts and reconstruction planning with high accuracy.

Contribution

It introduces a combined approach of ResNet and PSPNet models with regression analysis for comprehensive damage quantification from satellite images.

Findings

ResNet achieves 92% accuracy in identifying destruction areas.

PSPNet provides 84% accuracy in damage quantification.

System successfully matches damage predictions to the Beirut explosion recovery.

Abstract

Humanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here, we use satellite images before and after major crisis around the world to train a robust baseline Residual Network (ResNet) and a disaster quantification Pyramid Scene Parsing Network (PSPNet). ResNet offers robustness to poor image quality and can identify areas of destruction with high accuracy (92\%), whereas PSPNet offers contextualised quantification of built environment damage with good accuracy (84\%). As there are multiple damage dimensions to consider (e.g. economic loss and fatalities), we fit a multi-linear regression model to quantify the overall damage. To validate our combined system of deep learning and regression modeling, we successfully match our prediction to the ongoing recovery in the 2020 Beirut port explosion. These innovations provide a better quantification of overall disaster magnitude and inform intelligent humanitarian systems of unfolding disasters.