Abstract
Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.
Highlights
Over the past several decades, large-scale natural disasters have occurred much more frequently
Based on the above reasons, this paper proposes a Mask R-convolutional neural networks (CNNs)-based model combined with the improved feature pyramid networks (FPNs) presented by Liu et al [45] and the online hard example mining (OHEM)
We aim to develop a faster means to extract damaged buildings and estimate their damage levels using high resolution aerial images taken after the 2016
Summary
Over the past several decades, large-scale natural disasters have occurred much more frequently. According to the United Nations Office, disasters affected 4.2 billion people and resulted in approximately 2.97 trillion USD in economic losses globally [1]. To minimize economic loss and initiate appropriate rescue and recovery activities, a quick and accurate damage assessment is vital. A field survey could provide more detailed information, it requires tremendous manpower and time. Under such circumstances, remote sensing technology becomes an alternate way to collect damage information effectively. Remote sensing systems have various platforms (i.e., spaceborne, airborne, and groundbased) using optical, synthetic aperture radar (SAR), and laser imaging detection and ranging (LiDAR) images to assess damage caused by nature disasters [2,3,4,5].
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