Next Article in Journal
Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry
Next Article in Special Issue
Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut
Previous Article in Journal
Determination of Bio-Based Fertilizer Composition Using Combined NIR and MIR Spectroscopy: A Model Averaging Approach
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 22
Issue 15
10.3390/s22155920
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Zhonghua Hong
1,
Hongzheng Zhong
1,
Haiyan Pan
1,*,
Jun Liu
1,2,3,
Ruyan Zhou
1,
Yun Zhang
1,
Yanling Han
1,
Jing Wang
1,
Shuhu Yang
1 and
Changyue Zhong
3
1
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
2
National Earthquake Response Support Service, Beijing 100049, China
3
College of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(15), 5920; https://doi.org/10.3390/s22155920
Submission received: 28 June 2022 / Revised: 4 August 2022 / Accepted: 4 August 2022 / Published: 8 August 2022
(This article belongs to the Special Issue Data Fusion and Artificial Intelligence Applications in Remote Sensing)
Browse Figures
Review Reports Versions Notes

Abstract

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.
Keywords: building damage; deep learning; earthquake building damage classification net (EBDC-Net); aerial images building damage; deep learning; earthquake building damage classification net (EBDC-Net); aerial images

Share and Cite

MDPI and ACS Style

Hong, Z.; Zhong, H.; Pan, H.; Liu, J.; Zhou, R.; Zhang, Y.; Han, Y.; Wang, J.; Yang, S.; Zhong, C. Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images. Sensors 2022, 22, 5920. https://doi.org/10.3390/s22155920

AMA Style

Hong Z, Zhong H, Pan H, Liu J, Zhou R, Zhang Y, Han Y, Wang J, Yang S, Zhong C. Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images. Sensors. 2022; 22(15):5920. https://doi.org/10.3390/s22155920

Chicago/Turabian Style

Hong, Zhonghua, Hongzheng Zhong, Haiyan Pan, Jun Liu, Ruyan Zhou, Yun Zhang, Yanling Han, Jing Wang, Shuhu Yang, and Changyue Zhong. 2022. "Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images" Sensors 22, no. 15: 5920. https://doi.org/10.3390/s22155920

APA Style

Hong, Z., Zhong, H., Pan, H., Liu, J., Zhou, R., Zhang, Y., Han, Y., Wang, J., Yang, S., & Zhong, C. (2022). Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images. Sensors, 22(15), 5920. https://doi.org/10.3390/s22155920

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop