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

• URL: http://arxiv.org/abs/2511.06456v2
• Date: Thu, 13 Nov 2025 21:20:09 GMT
• Title: EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images
• Authors: Huili Huang, Chengeng Liu, Danrong Zhang, Shail Patel, Anastasiya Masalava, Sagar Sadak, Parisa Babolhavaeji, WeiHong Low, Max Mahdi Roozbahani, J. David Frost,
• Abstract summary: We introduce EIDSeg, the first large-scale semantic segmentation dataset specifically for post-earthquake social media imagery.<n>The dataset comprises 3,266 images from nine major earthquakes (2008-2023), annotated across five classes of infrastructure damage.<n>We benchmark several state-of-the-art segmentation models, identifying the only Mask Transformer (EoMT) as the top-performing method with a Mean Intersection over Union (mIoU) of 80.8%.
• Score: 0.5155683227758207
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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