  Vulnerability and risk assessment of neighborhoods is essential for effective
disaster preparedness. Existing traditional systems, due to dependency on
time-consuming and cost-intensive field surveying, do not provide a scalable
way to decipher warnings and assess the precise extent of the risk at a
hyper-local level. In this work, machine learning was used to automate the
process of identifying dwellings and their type to build a potentially more
effective disaster vulnerability assessment system. First, satellite imageries
of low-income settlements and vulnerable areas in India were used to identify 7
different dwelling types. Specifically, we formulated the dwelling type
classification as a semantic segmentation task and trained a U-net based neural
network model, namely TernausNet, with the data we collected. Then a risk score
assessment model was employed, using the determined dwelling type along with an
inundation model of the regions. The entire pipeline was deployed to multiple
locations prior to natural hazards in India in 2020. Post hoc ground-truth data
from those regions was collected to validate the efficacy of this model which
showed promising performance. This work can aid disaster response organizations
and communities at risk by providing household-level risk information that can
inform preemptive actions.
