Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption

Melanie Laverdiere; Lexie Yang; Mark Tuttle; Chris Vaughan

doi:10.1109/IGARSS39084.2020.9324160

Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption

Melanie Laverdiere, Lexie Yang, Mark Tuttle, Chris Vaughan

GeoAI

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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 Kİlauea 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.

Original language	English
Title of host publication	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6826-6829
Number of pages	4
ISBN (Electronic)	9781728163741
DOIs	https://doi.org/10.1109/IGARSS39084.2020.9324160
State	Published - Sep 26 2020
Event	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States Duration: Sep 26 2020 → Oct 2 2020

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/Territory	United States
City	Virtual, Waikoloa
Period	09/26/20 → 10/2/20

Funding

∗This manuscript has been authored by UT-Battelle, LLC under Contract No.DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paidup, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

Funders	Funder number
U.S. Department of Energy

Keywords

convolutional neural network
disaster response
structure mapping

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IGARSS39084.2020.9324160

Cite this

Laverdiere, M., Yang, L., Tuttle, M., & Vaughan, C. (2020). Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings (pp. 6826-6829). Article 9324160 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS39084.2020.9324160

Laverdiere, Melanie ; Yang, Lexie ; Tuttle, Mark et al. / Rapid Structure Detection in Support of Disaster Response : A Case Study of the 2018 Kilauea Volcano Eruption. 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 6826-6829 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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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 Kİlauea 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.",

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Laverdiere, M, Yang, L, Tuttle, M & Vaughan, C 2020, Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption. in 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings., 9324160, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 6826-6829, 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, Virtual, Waikoloa, United States, 09/26/20. https://doi.org/10.1109/IGARSS39084.2020.9324160

Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption. / Laverdiere, Melanie; Yang, Lexie; Tuttle, Mark et al.
2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 6826-6829 9324160 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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 Kİlauea 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.

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Laverdiere M, Yang L, Tuttle M, Vaughan C. Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption. In 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 6826-6829. 9324160. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS39084.2020.9324160

Rapid Structure Detection in Support of Disaster Response: A Case Study of the 2018 Kilauea Volcano Eruption

Abstract

Publication series

Conference

Funding

Keywords

UN SDGs

Access to Document

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