Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

Abstract

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.

BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

“…The works [29,41] concerned with developing methodologies to infer new disasters used model architectures similar to the ones described in the previous paragraph. Other studies extrapolated to different disasters types [32,42,43,44,45]. In many of these cases, transferability is dependent on the similarity between to the train and test regions [29] and is usually degraded by data heterogeneity [29,44].…”

“…Other studies extrapolated to different disasters types [32,42,43,44,45]. In many of these cases, transferability is dependent on the similarity between to the train and test regions [29] and is usually degraded by data heterogeneity [29,44]. Other solutions proposed including a small number of test samples with the training data [41,44], a multi-domain adaptive batch normalization and a stochastic weight averaging [42].…”

A Hybrid Intelligent Model for Urban Seismic Risk Assessment from the Perspective of Possibility and Vulnerability Based on Particle Swarm Optimization

“…e initial system consisted of 17 initial evaluation indicators, which were revised and improved following interviews and surveys with experts. Finally, we created a comprehensive vulnerability assessment index for urban seismic activity (Table 1) consisting of 13 evaluation indicators in the four criteria categories of building vulnerability, secondary disaster risk, socioeconomic vulnerability, and urban emergency response capabilities [45][46][47][48][49]. e vulnerability of buildings is largely a reflection of a city's exposure to earthquakes.…”

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

“…At present, with the development of big data and deep learning technologies, convolutional neural networks (CNN) are widely used in mapping ice-wedge polygon (IWP) [5,6], identifying damaged buildings [7], classifying sea ice cover and land type [8][9][10], and so on.…”