Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake
Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake
复制标题
用于识别地震受损建筑物的卷积神经网络模型的可迁移性
DOI：
10.3390/rs13030504
复制
发表时间：
2021-02-01
期刊：
影响因子：
5
通讯作者：
Luo, Peng
中科院分区：
文献类型：
--
作者：
Yang, Wanting;Zhang, Xianfeng;Luo, Peng
The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.
