Post-Disaster Damage Assessment Using Drones in a Remote Communication Setting

Ecem Yucesoy (), Elvin Coban () and Burcu Balcik ()
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Ecem Yucesoy: Ozyegin University
Elvin Coban: Ozyegin University
Burcu Balcik: Ozyegin University

A chapter in Handbook for Management of Threats, 2023, pp 299-323 from Springer

Abstract: Abstract After a disaster event, obtaining fast and accurate information about the damaged built-in structure is crucial for planning life-saving response operations. Unmanned aerial vehicles (UAVs), known otherwise as drones, are increasingly utilized to support damage assessment activities as a part of humanitarian operations. In this study, we focus on a post-disaster setting where the drones are utilized to scan a disaster-affected area to gather information on the damage levels. The affected area is assumed to be divided into grids with varying criticality levels. We consider en-route recharge stations to address battery limitations and remote information transmission to a single operation center. We address the problem of determining the routes of a set of drones across a given assessment horizon to maximize the number of visited grids considering their criticality levels and transmit the collected assessment information as quickly as possible along the routes. We propose a mixed integer linear programming formulation to solve this problem and also adapt it to a setting where the information transmission is only possible at the end of the routes for comparison purposes. We propose performance metrics to evaluate the performance of our model and present results on small-sized instances with sensitivity analysis. We present results that highlight the tradeoff between attained coverage (visiting more grids) and response time (the timing of information transmission in the scanned areas). Moreover, we show the advantage of en-route data transmission compared to the setting with data transmission at the end of the routes.

Keywords: Drone; Routing; Damage assessment; Remote communication; Disaster response (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-39542-0_15

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