Next Article in Journal
Monitoring Sea Level and Topography of Coastal Lagoons Using Satellite Radar Altimetry: The Example of the Arcachon Bay in the Bay of Biscay
Previous Article in Journal
A Side Scan Sonar Image Target Detection Algorithm Based on a Neutrosophic Set and Diffusion Maps
Article

Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

by 1,*,†, 2,†, 1,†, 1,†, 1,† and 3,†
1
International Research Institute of Disaster Science, Tohoku University, Aoba 468-I-E301, Aramaki, Aoba-ku, Sendai 980-0845, Japan
2
Graduate School of Information Science, Tohoku University, 6-6-05 Aramaki Aza Aoba, Aoba-ku, Sendai 980-8579, Japan
3
Department of Urban Environment Systems, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2018, 10(2), 296; https://doi.org/10.3390/rs10020296
Received: 4 January 2018 / Revised: 30 January 2018 / Accepted: 12 February 2018 / Published: 14 February 2018
Although supervised machine learning classification techniques have been successfully applied to detect collapsed buildings, there is still a major problem that few publications have addressed. The success of supervised machine learning strongly depends on the availability of training samples. Unfortunately, in the aftermath of a large-scale disaster, training samples become available only after several weeks or even months. However, following a disaster, information on the damage situation is one of the most important necessities for rapid search-and-rescue efforts and relief distribution. In this paper, a modification of the supervised machine learning classification technique called logistic regression is presented. Here, the training samples are replaced with probabilistic information, which is calculated from the spatial distribution of the hazard under consideration and one or more fragility functions. Such damage probabilities can be collected almost in real time for specific disasters such as earthquakes and/or tsunamis. We present the application of the proposed method to the 2011 Great East Japan Earthquake and Tsunami for collapsed building detection. The results show good agreement with a field survey performed by the Ministry of Land, Infrastructure, Transport and Tourism, with an overall accuracy of over 80%. Thus, the proposed method can significantly contribute to a rapid estimation of the number and locations of collapsed buildings. View Full-Text
Keywords: unsupervised classification; building damage; 2011 Great East Japan Earthquake and Tsunami unsupervised classification; building damage; 2011 Great East Japan Earthquake and Tsunami
Show Figures

Graphical abstract

MDPI and ACS Style

Moya, L.; Marval Perez, L.R.; Mas, E.; Adriano, B.; Koshimura, S.; Yamazaki, F. Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions. Remote Sens. 2018, 10, 296. https://doi.org/10.3390/rs10020296

AMA Style

Moya L, Marval Perez LR, Mas E, Adriano B, Koshimura S, Yamazaki F. Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions. Remote Sensing. 2018; 10(2):296. https://doi.org/10.3390/rs10020296

Chicago/Turabian Style

Moya, Luis, Luis R. Marval Perez, Erick Mas, Bruno Adriano, Shunichi Koshimura, and Fumio Yamazaki. 2018. "Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions" Remote Sensing 10, no. 2: 296. https://doi.org/10.3390/rs10020296

Find Other Styles
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
Back to TopTop