## 1 Introduction  
Immediate response and planning for rescue and reconstruction operations are essential after an earthquake. Field survey is time-consuming and costly and, in some cases, this is impossible due to road closures [1; 2; 3; 4]. Hence, several remote sensing datasets and methods have been proposed to accelerate this work. One of the best ways to assess earthquake damage is to use space borne and airborne information before and after the event [5]. Synthetic aperture radar (SAR) is one of the most powerful tools for monitoring natural and unnatural events on Earth that can collect information during the day and night without being affected by weather conditions. Other advantage of this monitoring system is fast observations on a large scale [6]. Airborne light detection and ranging (LIDAR) is another efficient remote sensing technology that measures distance by sending a pulsed laser at a target and analyzing the reflected light. LIDAR sensors collect data in the form of three-dimension cloud points. Landslide detection and damage assessment of buildings are the main capabilities of this method [7; 8; 9; 10; 11]. LIDARdata is mainly used to generate a high-resolution digital elevation model (DEM). Various studies have referred to the great capability of LIDAR images to assess building damages but the cost issues of this type of information and its dependence on weather conditions have made this type of information unavailable to some extent. Nevertheless, when it is available, this type of data is very valuable in assessing the damage [11; 12]. Using the combination of these two techniques or through each of them separately some research studies have been done. For example, [PERSON] et al. used multitemporal airborne LIDAR data to investigate landslides caused by the Kumamoto earthquake. Their study demonstrated the appropriate ability of this method to detect landslides [8]. [PERSON] et al. conducted research using pre- and post-event LIDAR images. They studied the parameters of correlation coefficient difference and elevation difference in both images to detect landslides and earthquake damage. Their results confirmed the advantage of this method in landslide detection [13]. [PERSON] et al. using SAR images and sequential coherence methods, conducted a study about the Sarpole-Zahab earthquake (Iran). They used 56 pre- and co-event SAR images and studied the effect of parameters such as seasonal changes and the height of buildings [1]. [PERSON] et al. used correlation coefficient, SAR interferometry and texture analysis methods to evaluate the damage of structures caused by Sarpole-Zahab earthquake. Using the random forest algorithm, they obtained an overall accuracy of 86.3% in evaluating damaged buildings [14]. [PERSON] et al. developed an automatic structural damage detection method using post-disaster aerial images and the deep learning method, about the 2016 Kumamoto and the 1995 Kobe earthquakes. Their results showed that this method achieved high accuracy in damage assessment [15]. Nowadays, various imaging and ground control systems have been developed. Along with their development, it is necessary to have methods for more accurate and faster analysis of information collected from these systems. Machine learning is one of the most popular tools in remote sensing and seismology, which is used to assess the damage to buildings. In this method, the response variables and the related explanatory variables, which can be qualitative or quantitative values, are inputted into the algorithm. The algorithm analyzes these inputs and finds the relation between the response variables and the related explanatory variables. It then categorizes the new input variables using the analysis performed [16]. [PERSON] et al. using ALOS-2/PALSAR-2 SAR images and applying the machine learning method, researched the 2016 Kumamoto earthquake. In this evaluation, they performed building damage mapping using only post-event SAR images and mapping using multi-temporal SAR images [6]. In addition, [PERSON] et al.