Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning

Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and...
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Autor*in:	Jinglin Xu [verfasserIn] Feng Zeng [verfasserIn] Wen Liu [verfasserIn] Toru Takahashi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	deep learning aerial photo typhoon faxai roof damage detection classification

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 12(2022), 10, p 4912
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:10, p 4912

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app12104912

Katalog-ID:	DOAJ031229727

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520			\|a Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.
650		4	\|a deep learning
650		4	\|a aerial photo
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650		4	\|a roof damage
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authorswithroles_txt_mv	Jinglin Xu @@aut@@ Feng Zeng @@aut@@ Wen Liu @@aut@@ Toru Takahashi @@aut@@
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callnumber-first	T - Technology
author	Jinglin Xu
spellingShingle	Jinglin Xu misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc deep learning misc aerial photo misc typhoon faxai misc roof damage misc detection misc classification misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning deep learning aerial photo typhoon faxai roof damage detection classification
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc deep learning misc aerial photo misc typhoon faxai misc roof damage misc detection misc classification misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning
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title_sort	damage detection and level classification of roof damage after typhoon faxai based on aerial photos and deep learning
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title_auth	Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning
abstract	Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.
abstractGer	Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.
abstract_unstemmed	Following the occurrence of a typhoon, quick damage assessment can facilitate the quick dispatch of house repair and disaster insurance works. Employing a deep learning method, this study used aerial photos of the Chiba prefecture obtained following Typhoon Faxai in 2019, to automatically detect and evaluate the roof damage. This study comprised three parts: training a deep learning model, detecting the roof damage using a trained model, and classifying the level of roof damage. The detection object comprised a roof outline, blue tarps, and a completely destroyed roof. The roofs were divided into three categories: without damage, with blue tarps, and completely destroyed. The F value obtained using the proposed method was higher than those obtained using other methods. In addition, it can be further divided into five levels from levels 0 to 4. Finally, the spatial distribution of the roof damage was analyzed using ArcGIS tools. The proposed method is expected to provide a certain reference for real-time detection of roof damage after the occurrence of a typhoon.
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title_short	Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning
url	https://doi.org/10.3390/app12104912 https://doaj.org/article/299e40c972524dcbab80305df1ab8e26 https://www.mdpi.com/2076-3417/12/10/4912 https://doaj.org/toc/2076-3417
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Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning

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