Abstract
Abstract. The increasing availability of very high resolution (VHR) remotely sensed images makes it possible to detect and assess urban building damages in the aftermath of earthquake disasters by using these data. However, the accuracy obtained using spectral features from VHR data alone is comparatively low, since both undamaged and collapsed buildings are spectrally similar. The height information provided by airborne LiDAR (Light Detection And Ranging) data is complementary to VHR imagery. Thus, combination of these two datasets will be beneficial to the automatic and accurate extraction of building collapse. In this study, a hierarchical multi-level method of building collapse detection using bi-temporal (pre- and post-earthquake) VHR images and postevent airborne LiDAR data was proposed. First, buildings, bare ground, vegetation and shadows were extracted using post-event image and LiDAR data and masked out. Then building collapse was extracted using the bi-temporal VHR images of the remaining area with a one-class classifier. The proposed method was evaluated using bi-temporal VHR images and LiDAR data of Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. The method was also compared with some existing methods. The results showed that the method proposed in this study significantly outperformed the existing methods, with improvement range of 47.6% in kappa coefficient. The proposed method provided a fast and reliable way of detecting urban building collapse, which can also be applied to relevant applications.
Highlights
And accurate post-earthquake damage information is of great importance to disaster assessment and management
The results of building collapse detection by different methods are listed in table 2
By using hierarchical multi-level method proposed in this study, both overall accuracy and kappa coefficient were largely improved
Summary
And accurate post-earthquake damage information is of great importance to disaster assessment and management. With the development of remote sensing technology, the availability of very high resolution (VHR) satellite imagery makes it possible to detect and assess building damage in the aftermath of earthquakes. Many studies have been focused on building damage detection these years. The fundamental principle of building damage detection is to automatically detect changes between bitemporal (pre- and post- earthquake) images of the quaked region. In these years, many studies have used LiDAR data and VHR images to extract building Few studies have used LiDAR data and VHR images to detect building collapse. In this study, we used Light Detection And Ranging (LiDAR) data to distinguish collapsed buildings, undamaged buildings and bare ground which have spectral similarities, as they have different heights
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