Next Article in Journal
The Rockall and the Orphan Basins of the Southern North Atlantic Ocean: Determining Continuous Basins across Conjugate Margins
Previous Article in Journal
The Functioning of Erosion-channel Systems of the River Basins of the South of Eastern Siberia
Previous Article in Special Issue
New Perspectives in the Definition/Evaluation of Seismic Hazard through Analysis of the Environmental Effects Induced by Earthquakes
Open AccessReview

Tsunami Damage Detection with Remote Sensing: A Review

by Shunichi Koshimura 1,*,†, Luis Moya 1,2, Erick Mas 1,† and Yanbing Bai 3
1
International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
2
Japan-Peru Center for Earthquake Engineering Research and Disaster Mitigation, National University of Engineering, Lima 15333, Peru
3
Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
*
Author to whom correspondence should be addressed.
Current address: International Research Institute of Disaster Science, Tohoku University, Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan.
Geosciences 2020, 10(5), 177; https://doi.org/10.3390/geosciences10050177
Received: 17 April 2020 / Accepted: 30 April 2020 / Published: 12 May 2020
Tsunamis are rare events compared with the other natural disasters, but once it happens, it can be extremely devastating to the coastal communities. Extensive inland penetration of tsunamis may cause the difficulties of understanding its impact in the aftermath of its generation. Therefore the social needs to technologies of detecting the wide impact of great tsunamis have been increased. Recent advances of remote sensing and technologies of image analysis meet the above needs and lead to more rapid and efficient understanding of tsunami affected areas. This paper provides a review of how remote sensing methods have developed to contribute to post-tsunami disaster response. The evaluations in the performances of the remote sensing methods are discussed according to the needs of tsunami disaster response with future perspective. View Full-Text
Keywords: tsunami; damage detection; remote sensing; machine learning; deep learning tsunami; damage detection; remote sensing; machine learning; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Koshimura, S.; Moya, L.; Mas, E.; Bai, Y. Tsunami Damage Detection with Remote Sensing: A Review. Geosciences 2020, 10, 177.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop