Accelerated Assessment of Critical Infrastructure in Aiding Recovery Efforts during Natural and Human-made Disaster

Gautam Thakur, Kelly Sims, Chantelle Rittmaier, Joseph Bentley, Debraj De, Junchuan Fan, Tao Liu, Rachel Palumbo, Jesse McGaha, Phil Nugent, Bryan Eaton, Jordan Burdette, Tyler Sheldon, Kevin Sparks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Relief and recovery from disasters (both natural and human-made) require a coordinated approach across several federal and state government agencies. In order to achieve optimal resource allocation and deployment of first responders, accurate and timely assessment of the impact and extent of destruction are the cornerstones to any recovery effort. Ideally, this knowledge should be gathered and shared within the first 0-24 hours (termed as "Acute Phase"by the U.S. CDC guideline) for informed decision-making. But achieving this poses significant challenges for the data collection and data harmonization processes, particularly when voluminous data are being generated from diverse and distributed sources during the disaster responses. To this end, this work developed a scalable and efficient workflow to dynamically collect and harmonize crowd-sourced geographic multi-modal data, and then assess critical infrastructure (CI) damaged during disaster events. We demonstrate the application of our framework with two real-world experiences in addressing post-disaster recovery efforts - for the Bahamas (Natural - due to Hurricane Dorian, 2019) and Beirut (Human-made - due to explosion caused by the ammonium nitrate stored in a warehouse, 2020). We have illustrated that a coordinated effort is needed for planning as well as for execution to achieve informed decision making.

Original languageEnglish
Title of host publication29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
EditorsXiaofeng Meng, Fusheng Wang, Chang-Tien Lu, Yan Huang, Shashi Shekhar, Xing Xie
PublisherAssociation for Computing Machinery
Pages195-206
Number of pages12
ISBN (Electronic)9781450386647
DOIs
StatePublished - Nov 2 2021
Event29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 - Virtual, Online, China
Duration: Nov 2 2021Nov 5 2021

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
Country/TerritoryChina
CityVirtual, Online
Period11/2/2111/5/21

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
U.S. Department of Energy

    Keywords

    • Spatial data mining and knowledge discovery
    • assessment of critical infrastructure
    • damage assessment
    • data curation and management
    • data reliability and quality
    • disaster response
    • geographic information retrieval
    • geographic information system
    • machine learning

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