Publicación:
Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification
Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification
| dc.contributor.author | Moya L. | es_PE |
| dc.contributor.author | Geis C. | es_PE |
| dc.contributor.author | Hashimoto M. | es_PE |
| dc.contributor.author | Mas E. | es_PE |
| dc.contributor.author | Koshimura S. | es_PE |
| dc.contributor.author | Strunz G. | es_PE |
| dc.date.accessioned | 2024-05-30T23:13:38Z | |
| dc.date.available | 2024-05-30T23:13:38Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake-tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings. © 1980-2012 IEEE. | |
| dc.description.sponsorship | Manuscript received March 30, 2020; revised August 4, 2020 and December 4, 2020; accepted December 10, 2020. Date of publication January 13, 2021; date of current version September 27, 2021. This work was supported in part by the National Fund for Scientific, Technological, and Technological Innovation Development (Fondecyt-Peru) within the framework of the “Project for the Improvement and Extension of the Services of the National System of Science, Technology and Technological Innovation” under Contract 038-2019, in part by the Japan Science and Technology Agency (JST) CREST Project under Grant JP-MJCR1411, in part by the Japan Society for the Promotion of Science (JSPS) Kakenhi under Grant 17H06108, in part by the Core Research Cluster of Disaster Science at Tohoku University (a Designated National University), and in part by the Helmholtz Association under Grant “pre_DICT” (PD-305). (Corresponding author: Luis Moya.) Luis Moya is with the Japan-Peru Center for Earthquake Engineering Research and Disaster Mitigation (CISMID), National University of Engineering, Lima 15333, Peru, and also with the International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai 980-8579, Japan (e-mail: lmoyah@uni.pe). | |
| dc.identifier.doi | https://doi.org/10.1109/TGRS.2020.3046004 | |
| dc.identifier.scopus | 2-s2.0-85099570132 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12390/2992 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | IEEE Transactions on Geoscience and Remote Sensing | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | support vector machine (SVM) | |
| dc.subject | Automatic labeling | es_PE |
| dc.subject | building damage | es_PE |
| dc.subject | multiregularization parameters | es_PE |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#1.05.044 | |
| dc.title | Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification | |
| dc.type | info:eu-repo/semantics/article | |
| dspace.entity.type | Publication | |
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