Datasets:
Formats:
imagefolder
Sub-tasks:
visual-question-answering
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 3,833 Bytes
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language:
- en
license: cc-by-sa-4.0
task_categories:
- visual-question-answering
- image-to-text
- question-answering
task_ids:
- visual-question-answering
modality:
- image
- text
tags:
- DisasterVQA
- disaster-response
- humanitarian
- crisis-informatics
- VQA
- VLM
- vision-language-models
- damage-assessment
- situational-awareness
source_datasets:
- Incidents1M
- CrisisMMD
- MEDIC
size_categories:
- 1K<n<10K
pretty_name: DisasterVQA
doi: 10.5281/zenodo.18365212
configs:
- config_name: default
data_files:
- split: train
path: DisasterVQA/**
dataset_info:
features:
- name: question_id
dtype: string
- name: image_id
dtype: string
- name: image
dtype: image
- name: question
dtype: string
- name: question_type
dtype: string
- name: groundtruth_answer
sequence: string
- name: choices
dtype:
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: disaster_type
dtype: string
- name: dataset_source
dtype: string
- name: region
dtype: string
- name: crisis_info_type
dtype: string
- name: crisis_info_code
dtype: string
---
# DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes
## Dataset Summary
DisasterVQA is a benchmark dataset for evaluating Vision-Language Models (VLMs) on disaster-response visual question answering. It contains **1,395 real-world disaster images** and **4,405 expert-curated question–answer pairs** covering floods, wildfires, and earthquakes.
The dataset includes three question types:
- **Binary** (Yes/No)
- **Multiple-Choice**
- **Open-Ended**
Questions span situational awareness and operational decision-making tasks, grounded in humanitarian frameworks (FEMA ESF, OCHA MIRA).
---
## Dataset Structure
### Repository Layout
```
├── README.md
├── disastervqa_annotations.jsonl
├── taxonomy.json
└── DisasterVQA/
├── Incidents1M/
│ └── ...
├── CrisisMMD/
│ └── ...
└── MEDIC/
└── ...
```
### Files
| File | Description |
|------|-------------|
| `disastervqa_annotations.jsonl` | Benchmark annotations and metadata (question text, ground-truth answers, image paths, taxonomy labels) |
| `taxonomy.json` | Final taxonomy definitions and references for each `crisis_info_code` |
| `DisasterVQA/Incidents1M/` | Disaster images sourced from the Incidents1M dataset |
| `DisasterVQA/CrisisMMD/` | Disaster images sourced from the CrisisMMD dataset |
| `DisasterVQA/MEDIC/` | Disaster images sourced from the MEDIC dataset |
---
## License
This dataset is released under the **Creative Commons Attribution Share Alike 4.0 International (CC BY-SA 4.0)** license.
---
## Citation
If you use this dataset, please cite the accompanying paper:
```bibtex
@inproceedings{disastervqa_icwsm2026,
author = {Al-Mohannadi, Aisha and Firoz, Ayisha and Yang, Yin and Imran, Muhammad and Ofli, Ferda},
title = {DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes},
booktitle = {Proceedings of the International AAAI Conference on Web and Social Media (ICWSM)},
year = {2026},
address = {Los Angeles, California, USA},
url = {https://arxiv.org/abs/2601.13839}
}
```
Paper: [arXiv:2601.13839](https://arxiv.org/abs/2601.13839)
---
## Links
- 📦 Zenodo: [https://doi.org/10.5281/zenodo.18267769](https://doi.org/10.5281/zenodo.18267769)
- 📄 Paper: [arXiv:2601.13839](https://arxiv.org/abs/2601.13839)
- 🏛️ Conference: ICWSM 2026, Los Angeles, California, USA
|