  Satellite images are an extremely valuable resource in the aftermath of
natural disasters such as hurricanes and tsunamis where they can be used for
risk assessment and disaster management. In order to provide timely and
actionable information for disaster response, in this paper a framework
utilising segmentation neural networks is proposed to identify impacted areas
and accessible roads in post-disaster scenarios. The effectiveness of
pretraining with ImageNet on the task of aerial image segmentation has been
analysed and performances of popular segmentation models compared. Experimental
results show that pretraining on ImageNet usually improves the segmentation
performance for a number of models. Open data available from OpenStreetMap
(OSM) is used for training, forgoing the need for time-consuming manual
annotation. The method also makes use of graph theory to update road network
data available from OSM and to detect the changes caused by a natural disaster.
Extensive experiments on data from the 2018 tsunami that struck Palu, Indonesia
show the effectiveness of the proposed framework. ENetSeparable, with 30% fewer
parameters compared to ENet, achieved comparable segmentation results to that
of the state-of-the-art networks.
