ABSTRACT:Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G 0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual-or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.
Volunteered geographic information (VGI) has been widely adopted as an alternative for authoritative geographic information in disaster management considering its up-to-date data. OpenStreetMap, in particular, is now aiming at crisis mapping for humanitarian purpose. This paper illustrated that natural disaster played an essential role in updating OpenStreetMap data after Haiti was hit by Hurricane Matthew in October, 2016. Spatial-temporal analysis of updated OSM data was conducted in this paper. Correlation of features was also studied to figure out whether updates of data were coincidence or the results of the hurricane. Spatial pattern matched the damaged areas and temporal changes fitted the time when disaster occurred. High level of correlation values of features were recorded when hurricane occurred, suggesting that updates in data were led by the hurricane.
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