Assessment of Post-earthquake Building Damage Using High-resolution Satellite Images and LiDAR Data - a Case Study From Port-au-prince, Haiti
Description: When an earthquake happens, one of the most important tasks of disaster managers is to conduct damage assessment; this is mostly done from remotely sensed data. This study presents a new method for building detection and damage assessment using high-resolution satellite images and LiDAR data from Port-au-Prince, Haiti. A graph-cut method is used for building detection due to its advantages compared to traditional methods such as the Hough transform. Results of two methods are compared to understand how much our proposed technique is effective. Afterwards, sensitivity analysis is performed to show the effect of image resolution on the efficiency of our method. Results are in four groups. First: based on two criteria for sensitivity analysis, completeness and correctness, the more efficient method is graph-cut, and the final building mask layer is used for damage assessment. Next, building damage assessment is done using change detection technique from two images from period of before and after the earthquake. Third, to integrate LiDAR data and damage assessment, we showed there is a strong relationship between terrain roughness variables that are calculated using digital surface models. Finally, open street map and normalized digital surface model are used to detect possible road blockages. Results of detecting road blockages showed positive values of normalized digital surface model on the road centerline can represent blockages if we exclude other objects such as cars.
Date: August 2014
Creator: Koohikamali, Mehrdad