# NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters

arXiv: https://arxiv.org/abs/2212.10735 · HTML: https://mlsanity.com/paper/2212.10735/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 21 of 21)

- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding](https://mlsanity.com/paper/2012.02951/) (2020)
- [PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison](https://mlsanity.com/paper/1703.00512/) (2017)
- [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)
- [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)
- [MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos](https://mlsanity.com/paper/2006.16479/) (2020)
- [Detecting natural disasters, damage, and incidents in the wild](https://mlsanity.com/paper/2008.09188/) (2020)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response](https://mlsanity.com/paper/2011.08916/) (2020)
- [Visual Sentiment Analysis from Disaster Images in Social Media](https://mlsanity.com/paper/2009.03051/) (2020)
- [FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection](https://mlsanity.com/paper/2112.08598/) (2021)
- [Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery](https://mlsanity.com/paper/1812.05581/) (2018)
- [Hephaestus: A large scale multitask dataset towards InSAR understanding](https://mlsanity.com/paper/2204.09435/) (2022)
- [EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts](https://mlsanity.com/paper/2012.06246/) (2020)
- [Active Learning for Event Detection in Support of Disaster Analysis Applications](https://mlsanity.com/paper/1909.12601/) (2019)
- [VIDI: A Video Dataset of Incidents](https://mlsanity.com/paper/2205.13277/) (2022)
- [DeepAg: Deep Learning Approach for Measuring the Effects of Outlier Events on Agricultural Production and Policy](https://mlsanity.com/paper/2110.12062/) (2021)

## Built on by (most cited first, 4 of 4)

- [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://mlsanity.com/paper/2307.01909/) (2023)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method](https://mlsanity.com/paper/2504.07394/) (2025)
- [AI for Anticipatory Action: Moving Beyond Climate Forecasting](https://mlsanity.com/paper/2307.15727/) (2023)

## Abstract (arXiv)

Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning (ML) in this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing them according to the disaster management cycle. We compile a list of existing benchmark datasets introduced in the past five years. We propose a web platform - NADBenchmarks - where researchers can search for benchmark datasets for natural disasters, and we develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions for topics where they can contribute new benchmark datasets.