TY - GEN
T1 - Accelerated Assessment of Critical Infrastructure in Aiding Recovery Efforts during Natural and Human-made Disaster
AU - Thakur, Gautam
AU - Sims, Kelly
AU - Rittmaier, Chantelle
AU - Bentley, Joseph
AU - De, Debraj
AU - Fan, Junchuan
AU - Liu, Tao
AU - Palumbo, Rachel
AU - McGaha, Jesse
AU - Nugent, Phil
AU - Eaton, Bryan
AU - Burdette, Jordan
AU - Sheldon, Tyler
AU - Sparks, Kevin
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/11/2
Y1 - 2021/11/2
N2 - 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.
AB - 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.
KW - Spatial data mining and knowledge discovery
KW - assessment of critical infrastructure
KW - damage assessment
KW - data curation and management
KW - data reliability and quality
KW - disaster response
KW - geographic information retrieval
KW - geographic information system
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85119177199&partnerID=8YFLogxK
U2 - 10.1145/3474717.3483947
DO - 10.1145/3474717.3483947
M3 - Conference contribution
AN - SCOPUS:85119177199
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 195
EP - 206
BT - 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
A2 - Meng, Xiaofeng
A2 - Wang, Fusheng
A2 - Lu, Chang-Tien
A2 - Huang, Yan
A2 - Shekhar, Shashi
A2 - Xie, Xing
PB - Association for Computing Machinery
T2 - 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021
Y2 - 2 November 2021 through 5 November 2021
ER -