DOI: 10.17603/ds2-ankb-yk64
title: Earthquake Infrastructure Damage Semantic Segmentation(EIDSeg) Dataset for Social Media Images, in EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images

description: The EIDSeg Dataset (Earthquake Infrastructure Damage Segmentation Dataset) is a large-scale, pixel-level annotated dataset designed for semantic segmentation of post-earthquake infrastructure damage using ground-level social media imagery. The dataset provides a unique resource for advancing computer vision and machine learning applications in disaster response and damage assessment. EIDSeg contains 3,266 ground-level images collected from nine major earthquake events between 2008 and 2023, covering a wide range of geographic regions, building types, and damage severities. Each image is manually annotated at the pixel level into five semantic classes that represent key types of infrastructure and damage states: Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. The dataset is organized into train, validation, and test subsets, each containing images and corresponding annotation files in CVAT XML format. EIDSeg provides a standardized resource to support research on fine-grained damage assessment, computer vision for disaster response, and domain-adaptive segmentation models. More details of the paper and code is updated in https://github.com/HUILIHUANG413/EIDSeg. 
description: This project investigated how ground-level images shared on social media can support detailed, pixel-level assessment of earthquake damage. Existing post-earthquake mapping approaches often rely on aerial or satellite imagery, which provide limited resolution, require specialized collection processes, and are expensive to obtain immediately after a disaster. In contrast, social media platforms contain thousands of ground-level photos taken by community members, first responders, or local residents. However, there has been no large, publicly available dataset that provides consistent pixel-level damage annotations for such imagery. This work set out to fill that gap by constructing EIDSeg, a large-scale semantic segmentation dataset focused on infrastructure damage visible in ground-level images.

To build the dataset, the authors collected 3,266 images from nine major earthquakes that occurred between 2008 and 2023. Each image was annotated into five classes: Undamaged Building, Damaged Building, Destroyed Building, Undamaged Road, and Damaged Road. Because most annotators were not domain experts, the team designed a three-phase cross-disciplinary annotation protocol that provided task guidelines, visual examples, and iterative reviews. This protocol helped standardize the labeling process and resulted in an inter-annotator agreement above seventy percent, which indicates strong reliability of the final labels.

After constructing the dataset, the authors tested its usefulness by benchmarking several state-of-the-art semantic segmentation models. These experiments demonstrated that modern vision architectures can effectively learn from social media imagery when pixel-level labels are available. Among all tested models, the Encoder-only Mask Transformer achieved the highest mean Intersection over Union, approximately 80 percent. This outcome confirmed that EIDSeg is a suitable resource for training automated systems that can detect and segment building and road damage from ground-level earthquake imagery.

The resulting dataset can be reused in many research and operational contexts. It provides a benchmark for developing new segmentation models, loss functions, or data augmentation strategies. Because the images come from real social media posts with diverse lighting, camera quality, and scene complexity, the dataset also supports research on model robustness, domain generalization, and adaptation across earthquake events. Agencies and organizations working on disaster response can use models trained on EIDSeg to support rapid damage screening when ground-level photos become available online. The dataset can also be integrated with aerial or satellite data for multi-modal mapping, combined with text-based situational reports, or used to study infrastructure vulnerability patterns across multiple earthquakes. Furthermore, the detailed annotation protocol offers a guideline for building similar datasets for other natural hazards, including floods, wildfires, and landslides.