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Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images

The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical need...

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Detalles Bibliográficos
Autores principales: Hong, Zhonghua, Zhong, Hongzheng, Pan, Haiyan, Liu, Jun, Zhou, Ruyan, Zhang, Yun, Han, Yanling, Wang, Jing, Yang, Shuhu, Zhong, Changyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371387/
https://www.ncbi.nlm.nih.gov/pubmed/35957476
http://dx.doi.org/10.3390/s22155920
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author Hong, Zhonghua
Zhong, Hongzheng
Pan, Haiyan
Liu, Jun
Zhou, Ruyan
Zhang, Yun
Han, Yanling
Wang, Jing
Yang, Shuhu
Zhong, Changyue
author_facet Hong, Zhonghua
Zhong, Hongzheng
Pan, Haiyan
Liu, Jun
Zhou, Ruyan
Zhang, Yun
Han, Yanling
Wang, Jing
Yang, Shuhu
Zhong, Changyue
author_sort Hong, Zhonghua
collection PubMed
description The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network—namely, the earthquake building damage classification net (EBDC-Net)—for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively.
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spelling pubmed-93713872022-08-12 Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images Hong, Zhonghua Zhong, Hongzheng Pan, Haiyan Liu, Jun Zhou, Ruyan Zhang, Yun Han, Yanling Wang, Jing Yang, Shuhu Zhong, Changyue Sensors (Basel) Article The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories—intact and damaged—which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network—namely, the earthquake building damage classification net (EBDC-Net)—for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively. MDPI 2022-08-08 /pmc/articles/PMC9371387/ /pubmed/35957476 http://dx.doi.org/10.3390/s22155920 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hong, Zhonghua
Zhong, Hongzheng
Pan, Haiyan
Liu, Jun
Zhou, Ruyan
Zhang, Yun
Han, Yanling
Wang, Jing
Yang, Shuhu
Zhong, Changyue
Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
title Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
title_full Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
title_fullStr Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
title_full_unstemmed Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
title_short Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images
title_sort classification of building damage using a novel convolutional neural network based on post-disaster aerial images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371387/
https://www.ncbi.nlm.nih.gov/pubmed/35957476
http://dx.doi.org/10.3390/s22155920
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