From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images

TL;DR

This paper introduces a semantic segmentation approach using social media images to objectively assess earthquake damage severity, improving accuracy and detail over traditional classification methods for disaster response.

Contribution

It proposes framing damage assessment as a semantic segmentation task, creating a new dataset, and developing a damage scoring system that considers damage extent and depth, advancing post-earthquake analysis.

Findings

Effective damage segmentation achieved with fine-tuned SegFormer model

New damage severity scoring system quantifies damage with depth adjustment

Enhanced damage assessment accuracy for disaster response

Abstract

In the aftermath of earthquakes, social media images have become a crucial resource for disaster reconnaissance, providing immediate insights into the extent of damage. Traditional approaches to damage severity assessment in post-earthquake social media images often rely on classification methods, which are inherently subjective and incapable of accounting for the varying extents of damage within an image. Addressing these limitations, this study proposes a novel approach by framing damage severity assessment as a semantic segmentation problem, aiming for a more objective analysis of damage in earthquake-affected areas. The methodology involves the construction of a segmented damage severity dataset, categorizing damage into three degrees: undamaged structures, damaged structures, and debris. Utilizing this dataset, the study fine-tunes a SegFormer model to generate damage severity segmentations for post-earthquake social media images. Furthermore, a new damage severity scoring system is introduced, quantifying damage by considering the varying degrees of damage across different areas within images, adjusted for depth estimation. The application of this approach allows for the quantification of damage severity in social media images in a more objective and comprehensive manner. By providing a nuanced understanding of damage, this study enhances the ability to offer precise guidance to disaster reconnaissance teams, facilitating more effective and targeted response efforts in the aftermath of earthquakes.