  New crisis response and management approaches that incorporate the latest
information technologies are essential in all phases of emergency preparedness
and response, including the planning, response, recovery, and assessment
phases. Accurate and timely information is as crucial as is rapid and coherent
coordination among the responding organizations. We are working towards a
multipronged emergency response tool that provide stakeholders timely access to
comprehensive, relevant, and reliable information. The faster emergency
personnel are able to analyze, disseminate and act on key information, the more
effective and timelier their response will be and the greater the benefit to
affected populations. Our tool consists of encoding multiple layers of open
source geospatial data including flood risk location, road network strength,
inundation maps that proxy inland flooding and computer vision semantic
segmentation for estimating flooded areas and damaged infrastructure. These
data layers are combined and used as input data for machine learning algorithms
such as finding the best evacuation routes before, during and after an
emergency or providing a list of available lodging for first responders in an
impacted area for first. Even though our system could be used in a number of
use cases where people are forced from one location to another, we demonstrate
the feasibility of our system for the use case of Hurricane Florence in
Lumberton, North Carolina.
