  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.
