# Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response

arXiv: https://arxiv.org/abs/2011.08916 · HTML: https://mlsanity.com/paper/2011.08916/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks](https://mlsanity.com/paper/2104.03090/) (2021)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and method](https://mlsanity.com/paper/2410.08814/) (2024)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [ArMeme: Propagandistic Content in Arabic Memes](https://mlsanity.com/paper/2406.03916/) (2024)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters](https://mlsanity.com/paper/2212.10735/) (2022)
- [A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media](https://mlsanity.com/paper/2405.00903/) (2024)
- [Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data](https://mlsanity.com/paper/2202.00050/) (2022)
- [DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition](https://mlsanity.com/paper/2410.13663/) (2024)
- [Image Captioners Sometimes Tell More Than Images They See](https://mlsanity.com/paper/2305.02932/) (2023)
- [Multi-Similarity Contrastive Learning](https://mlsanity.com/paper/2307.02712/) (2023)
- [Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content](https://mlsanity.com/paper/2112.12060/) (2021)
- [Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster](https://mlsanity.com/paper/2302.13403/) (2023)
- [Reading Is Believing: Revisiting Language Bottleneck Models for Image Classification](https://mlsanity.com/paper/2406.15816/) (2024)
- [Multimodal Learning with Augmentation Techniques for Natural Disaster Assessment](https://mlsanity.com/paper/2511.00004/) (2025)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)
- [AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes](https://mlsanity.com/paper/2607.27393/) (2026)

## Abstract (arXiv)

During a disaster event, images shared on social media helps crisis managers gain situational awareness and assess incurred damages, among other response tasks. Recent advances in computer vision and deep neural networks have enabled the development of models for real-time image classification for a number of tasks, including detecting crisis incidents, filtering irrelevant images, classifying images into specific humanitarian categories, and assessing the severity of damage. Despite several efforts, past works mainly suffer from limited resources (i.e., labeled images) available to train more robust deep learning models. In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment. Moreover, we relabel existing publicly available datasets for new tasks. We identify exact- and near-duplicates to form non-overlapping data splits, and finally consolidate them to create larger datasets. In our extensive experiments, we benchmark several state-of-the-art deep learning models and achieve promising results. We release our datasets and models publicly, aiming to provide proper baselines as well as to spur further research in the crisis informatics community.