Improving Community Resiliency and Emergency Response With Artificial Intelligence

• URL: http://arxiv.org/abs/2005.14212v1
• Date: Thu, 28 May 2020 18:05:08 GMT
• Title: Improving Community Resiliency and Emergency Response With Artificial Intelligence
• Authors: Ben Ortiz and Laura Kahn and Marc Bosch and Philip Bogden and Viveca Pavon-Harr and Onur Savas and Ian McCulloh
• Abstract summary: We are working towards a multipronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. 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.
• Score: 0.05541644538483946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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