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Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification

Moya, Luis and Geiß, Christian and Hashimoto, Masakazu and Mas, Erick and Koshimura, Shunichi and Strunz, Günter (2021) Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification. IEEE Transactions on Geoscience and Remote Sensing, 59 (10), pp. 8288-8304. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3046004. ISSN 0196-2892.

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Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9321713

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.

Item URL in elib:https://elib.dlr.de/144224/
Document Type:Article
Title:Disaster Intensity-Based Selection of Training Samples for Remote Sensing Building Damage Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Moya, LuisJapan-Peru Center for Earthquake Engineering Research and Disaster Mitigation (CISMID)UNSPECIFIED
Geiß, ChristianChristian.Geiss (at) dlr.deUNSPECIFIED
Hashimoto, MasakazuInternational Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, JapanUNSPECIFIED
Mas, ErickDisaster Control Research Center, TOHOKU University, Sendai, JapanUNSPECIFIED
Koshimura, ShunichiInternational Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, JapanUNSPECIFIED
Strunz, GünterGuenter.Strunz (at) dlr.deUNSPECIFIED
Date:2021
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:59
DOI :10.1109/TGRS.2020.3046004
Page Range:pp. 8288-8304
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Remote Sensing, Building Damage Classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Strunz, Dr.-Ing. Günter
Deposited On:04 Oct 2021 13:58
Last Modified:04 Oct 2021 13:58

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