# Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence

arXiv: https://arxiv.org/abs/2004.06675 · HTML: https://mlsanity.com/paper/2004.06675/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [CrisisViT: A Robust Vision Transformer for Crisis Image Classification](https://mlsanity.com/paper/2401.02838/) (2024)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [Landslide Detection in Real-Time Social Media Image Streams](https://mlsanity.com/paper/2110.04080/) (2021)
- [A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence](https://mlsanity.com/paper/2202.07475/) (2022)
- [RelAItionship Building: Analyzing Recruitment Strategies for Participatory AI](https://mlsanity.com/paper/2508.20176/) (2025)

## Abstract (arXiv)

Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.