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Title: RAPID STRUCTURE DETECTION IN SUPPORT OF DISASTER RESPONSE : A CASE STUDY OF THE 2018 KILAUEA VOLCANO ERUPTION

Abstract

Disaster response requires timely damage assessment to prioritize rescue and restoration resources. However, providing critical and actionable knowledge after a natural disaster can be challenging due to the scale and the type of damages. This paper describes how remote sensing and machine learning techniques can be used to support rapid structure detection in the wake of a disaster. We use high resolution satellite imagery to identify structures on Hawaii’s Big Island to support the Federal Emergency Management Agency’s response efforts during the 2018 K¯ilauea lava flow incident. This framework specifically showcases the generalizability of CNN models with no need to collect additional training samples to quickly map structures in pre- and post-event imagery and provide timely information to assist government agencies evaluating the extent and potential loss of disaster. With this case study, we further point out future directions to benefit similar larger scale efforts based on the lessons learned.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]
  1. ORNL
  2. FEMA
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1671424
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Geoscience and Remote Sensing Symposium (IEEE IGARSS) - Waikoloa, Hawaii, United States of America - 9/26/2020 4:00:00 PM-10/2/2020 4:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Laverdiere, Melanie, Yang, Lexie, Tuttle, Mark, and Vaughan, Chris. RAPID STRUCTURE DETECTION IN SUPPORT OF DISASTER RESPONSE : A CASE STUDY OF THE 2018 KILAUEA VOLCANO ERUPTION. United States: N. p., 2020. Web.
Laverdiere, Melanie, Yang, Lexie, Tuttle, Mark, & Vaughan, Chris. RAPID STRUCTURE DETECTION IN SUPPORT OF DISASTER RESPONSE : A CASE STUDY OF THE 2018 KILAUEA VOLCANO ERUPTION. United States.
Laverdiere, Melanie, Yang, Lexie, Tuttle, Mark, and Vaughan, Chris. 2020. "RAPID STRUCTURE DETECTION IN SUPPORT OF DISASTER RESPONSE : A CASE STUDY OF THE 2018 KILAUEA VOLCANO ERUPTION". United States. https://www.osti.gov/servlets/purl/1671424.
@article{osti_1671424,
title = {RAPID STRUCTURE DETECTION IN SUPPORT OF DISASTER RESPONSE : A CASE STUDY OF THE 2018 KILAUEA VOLCANO ERUPTION},
author = {Laverdiere, Melanie and Yang, Lexie and Tuttle, Mark and Vaughan, Chris},
abstractNote = {Disaster response requires timely damage assessment to prioritize rescue and restoration resources. However, providing critical and actionable knowledge after a natural disaster can be challenging due to the scale and the type of damages. This paper describes how remote sensing and machine learning techniques can be used to support rapid structure detection in the wake of a disaster. We use high resolution satellite imagery to identify structures on Hawaii’s Big Island to support the Federal Emergency Management Agency’s response efforts during the 2018 K¯ilauea lava flow incident. This framework specifically showcases the generalizability of CNN models with no need to collect additional training samples to quickly map structures in pre- and post-event imagery and provide timely information to assist government agencies evaluating the extent and potential loss of disaster. With this case study, we further point out future directions to benefit similar larger scale efforts based on the lessons learned.},
doi = {},
url = {https://www.osti.gov/biblio/1671424}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {9}
}

Conference:
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