Abstract
Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings.
Highlights
With the advance of sensor and space technology, remote sensing is able to obtain detailed temporal and spatial information at the target area, and has been widely used to detect, identify, and monitor the effect of natural disasters [1,2]
A variety of algorithms and parameters were tested on post-event aerial imagery for the earthquake in Christchurch, New Zealand, and the results showed that object-based approaches can produce better results than pixel-based approaches in earthquake damage detection using remotely sensed images [32]
Too large or small buildings were ignored by defining the thresholds, and the remaining buildings were padded by zero values to have the same dimensions
Summary
With the advance of sensor and space technology, remote sensing is able to obtain detailed temporal and spatial information at the target area, and has been widely used to detect, identify, and monitor the effect of natural disasters [1,2]. Building damage can be detected by using only post-event data with the help of the emergence of very high resolution (VHR) remote sensing imagery, which can provide detailed textural and spatial features of the damaged targets [8]. A wide range of remote sensing techniques is applicable to evaluate post-earthquake damage, including optical satellite imagery, synthetic aperture radar (SAR), and light detection and ranging (LiDAR). To validate and analyze the results, a validation map was created based on optical imagery, and the result demonstrated that SAR data have potential for application in urban disaster monitoring and assessment [25]
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