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

arXiv: https://arxiv.org/abs/2507.02781 · HTML: https://mlsanity.com/paper/2507.02781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [A Survey of Semantic Segmentation](https://mlsanity.com/paper/1602.06541/) (2016)
- [MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos](https://mlsanity.com/paper/2006.16479/) (2020)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [From Satellite Imagery to Disaster Insights](https://mlsanity.com/paper/1812.07033/) (2018)
- [Localizing and Quantifying Damage in Social Media Images](https://mlsanity.com/paper/1806.07378/) (2018)
- [Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2105.14540/) (2021)
- [Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data](https://mlsanity.com/paper/2202.00050/) (2022)

## Built on by (most cited first, 1 of 1)

- [EIDSeg: A Pixel-Level Semantic Segmentation Dataset for Post-Earthquake Damage Assessment from Social Media Images](https://mlsanity.com/paper/2511.06456/) (2025)

## Abstract (arXiv)

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.