Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling

Abstract. After an earthquake, efficiently and accurately acquiring information about damaged buildings can help reduce casualties. Earth observation data have been widely used to map affected areas after earthquakes. However, accurate post-earthquake assessment results are needed to manage recovery and reconstruction and estimate economic losses. In this paper, for quantification and precision purposes, information on earthquake-induced building damage is extracted using multi-source remote sensing images collected after an earthquake. The multi-source remote sensing data include optical data, synthetic aperture radar (SAR) data, and digital surface model (DSM) data generated by interpolating light detection and ranging (LiDAR) point cloud data. Features that describe texture, colour, and geometry are included in our analysis. The feature analysis is carried out according to the rough set theory to further determine the feature parameters. A logistic regression model (LRM) is employed to find the optimal fitting function to describe the relationship between the occurrence and absence of destroyed buildings within an individual object. In our experiment, old Beichuan County, China, the area most devastated by the Wenchuan earthquake on May 12, 2008, is used to test the proposed hypothesis. Through comparison with a ground survey, the experimental results show that the detection accuracy of the proposed method is 94.2 %; the area under the receiver operating characteristic (ROC) curve is 0.827. The efficiency of the proposed method is demonstrated using 6 modes of data combination acquired from the same area in old Beichuan County. The approach is one of the first attempts to extract damaged buildings through the fusion of three types of data with different features. The approach addresses multivariate regression methodologies and compares the potentials of different features for application in the field of damage detection.