TY - GEN
T1 - A Fully Automatic Method for Rapidly Mapping Impacted Area by Natural Disaster
AU - Liu, Tao
AU - Yang, Lexie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Deep learning based change detection methods have achieved the state-of-the-art performance in several recent studies. However, such methods usually are supervised, and therefore a large number of training samples is often a requisite. Manually preparing those training samples is not only expensive but also time-consuming, which does not fit the need of rapidly mapping the impacted area caused by nature disaster for further rescue mission and damage assessment. In this study, a fully automatic method was proposed to address the issue by automating training sample generation for mapping the impacted area caused by nature disaster. We used the 2011 tornado event in Joplin, Missouri, US, as an example of its application. The generated impacted area map was both visually and quantitatively evaluated against the ground truth data collected by US Federal Emergency Management Agency (FEMA). The results show that the map matches well with the FEMA ground truth data with 86% of major-damaged and destroyed buildings identified by FEMA on the ground also detected by this fully automatic framework using very high resolution (VHR) satellite images.
AB - Deep learning based change detection methods have achieved the state-of-the-art performance in several recent studies. However, such methods usually are supervised, and therefore a large number of training samples is often a requisite. Manually preparing those training samples is not only expensive but also time-consuming, which does not fit the need of rapidly mapping the impacted area caused by nature disaster for further rescue mission and damage assessment. In this study, a fully automatic method was proposed to address the issue by automating training sample generation for mapping the impacted area caused by nature disaster. We used the 2011 tornado event in Joplin, Missouri, US, as an example of its application. The generated impacted area map was both visually and quantitatively evaluated against the ground truth data collected by US Federal Emergency Management Agency (FEMA). The results show that the map matches well with the FEMA ground truth data with 86% of major-damaged and destroyed buildings identified by FEMA on the ground also detected by this fully automatic framework using very high resolution (VHR) satellite images.
KW - OBIA
KW - RANSAC
KW - SIFT
KW - change detection
KW - deep learning
KW - disaster assessment
UR - http://www.scopus.com/inward/record.url?scp=85099805256&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9323634
DO - 10.1109/IGARSS39084.2020.9323634
M3 - Conference contribution
AN - SCOPUS:85099805256
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6906
EP - 6909
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -