EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images

TL;DR

EIDSeg is a large-scale, pixel-level annotated dataset of social media images for post-earthquake damage assessment, enabling more detailed and rapid damage analysis beyond traditional aerial imagery.

Contribution

The paper introduces EIDSeg, the first large-scale semantic segmentation dataset for post-earthquake social media images, with a practical annotation protocol and benchmark results.

Findings

Encoder-only Mask Transformer (EoMT) achieved 80.8% mIoU.

Over 70% inter-annotator agreement with the proposed protocol.

Dataset covers nine major earthquakes with five damage classes.

Abstract

Rapid post-earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation. Although ground-level images from social networks provide a valuable source to fill this gap, a large pixel-level annotated dataset for this task is still unavailable. We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery. The dataset comprises 3,266 images from nine major earthquakes (2008-2023), annotated across five classes of infrastructure damage: Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. We propose a practical three-phase cross-disciplinary annotation protocol with labeling guidelines that enables consistent segmentation by non-expert annotators, achieving over 70% inter-annotator agreement. We benchmark several state-of-the-art segmentation models, identifying Encoder-only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%. By unlocking social networks' rich ground-level perspective, our work paves the way for a faster, finer-grained damage assessment in the post-earthquake scenario.