Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images.
Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images.
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使用基于灾后航空图像的新型卷积神经网络对建筑物损坏进行分类
DOI：
10.3390/s22155920
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发表时间：
2022-08-08
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影响因子：
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中科院分区：
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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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DOI：
10.1109/tgrs.2020.3014312
发表时间：
2021-05-01
影响因子：
8.2
作者：
Guo, Haonan;Shi, Qian;Ding, Huaxiang
通讯作者：
Ding, Huaxiang
组合多尺度分割卷积神经网络用于震后极高分辨率图像的快速损伤映射
影响因子：
1.7
作者：
Huang, Hui;Sun, Genyun;Ma, Hongzhang
通讯作者：
Ma, Hongzhang
影响因子：
5
作者：
Nex, Francesco;Duarte, Diogo;Kerle, Norman
通讯作者：
Kerle, Norman
影响因子：
5
作者：
Yang, Wanting;Zhang, Xianfeng;Luo, Peng
通讯作者：
Luo, Peng
影响因子：
12
作者：
Liu, Xiaolong;Deng, Zhidong;Yang, Yuhan
通讯作者：
Yang, Yuhan