DisasterVQA

# 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