# DisasterNets: Embedding Machine Learning in Disaster Mapping

arXiv: https://arxiv.org/abs/2306.09815 · HTML: https://mlsanity.com/paper/2306.09815/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Universal Domain Adaptation for Remote Sensing Image Scene Classification](https://mlsanity.com/paper/2301.11387/) (2023)

## Built on by (most cited first, 3 of 3)

- [On the Foundations of Earth and Climate Foundation Models](https://mlsanity.com/paper/2405.04285/) (2024)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [Large-scale flood modeling and forecasting with FloodCast](https://mlsanity.com/paper/2403.12226/) (2024)

## Abstract (arXiv)

Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed DisasterNets. It consists of two stages, space granulation and attribute granulation. The space granulation stage leverages supervised/semi-supervised learning, unsupervised change detection, and domain adaptation with/without source data techniques to handle different disaster mapping scenarios. Furthermore, the disaster database with the corresponding geographic information field properties is built by using the attribute granulation stage. The framework is applied to earthquake-triggered landslide mapping and large-scale flood mapping. The results demonstrate a competitive performance for high-precision, high-efficiency, and cross-scene recognition of disasters. To bridge the gap between disaster mapping and machine learning communities, we will provide an openly accessible tool based on DisasterNets. The framework and tool will be available at https://github.com/HydroPML/DisasterNets.