###### Abstract
A stochastic model can be created based on measurement parameters [14; 15; 16; 18], or a deterministic channel model can be created in combination with ray tracing [19]. For evaluating the performance of the communication systems in the air domain, it is necessary to select and reflect a channel model suitable for the area to be simulated among the channel models for each terrain environment. The task to be solved in this paper is automatically identifying the topography type of the performance simulation target area by using digital topographical information. Based on this, a channel model corresponding to the simulation area is automatically selected and used to simulate communication system performance. There are several types of topographical information, such as the Digital Elevation Model (DEM) and the Digital Surface Model (DSM) [20]. However, we use DSM in this paper because only DSM reflects ground structures such as buildings and trees, and it is open to the public [21]. An appropriate channel model for a specific region can be determined by human intuition based on the information known in advance on the region. However, it is very cumbersome when analyzing the network capacity for a wide area corresponding to a specific state or country through repeated simulations. Although it is possible to simulate communication performance in a small area, it is very cumbersome when analyzing the network capacity for a wide area corresponding to a specific state or country through repeated simulations. For example, to develop the communication network for UAM, it is necessary to simulate a wide area of airways corresponding to a distance of more than hundreds of kilometers. For another example, to optimize the ground stations' position by simulating the UAV command and control network coverage such as CNPC, it is necessary to select a channel model suitable for each detailed area's topography and apply it to the simulation. If a simulation is performed by selecting a channel model suitable for the topography of the corresponding area for each 10 square-kilometer area, about 10,000 selections are required for South Korea, and about 42,000 selections are necessary for the state of California. A person can't decide one by one, and a method for automatically selecting a channel model suitable for each experimental topography among multiple channel models is essential. For the automatic channel model selection method, there is a method using the correlation coefficient between the test area's altitude probability mass function (PMF) and the altitude PMF of the region where the actual measurement was performed for each channel model [22]. Even with the same topography, the absolute altitude varies significantly from country to country. Therefore, in [22], instead of the absolute-altitude PMF, the relative-altitude PMF, calculated for a constant number of steps between the maximum and minimum altitudes of each test area, was used for calculating the correlation values. In addition, it was shown that the geographic area of Korea could be well mapped with the topographical environments in which the channel models were developed. However, only a limited number of examples were presented to show the possibility of application. Except for the results in [22], there is no research result on the automatic topographic classification method. Machine learning (ML)technology is widely used in various academic fields, and its applicability to UAV communication is also being studied [23; 24]. In this paper, we proposed a method of classifying the topography using the well-known convolutional neural network (CNN) and deep neural network (DNN) methods using DSM data from various regions in Korea. In particular, we propose a two-step neural network (NN) method in which the classification process consists of successive steps utilizing two separate NN models to improve classification performance. Suppose the classification error is significant in the specific topography classes in the first step.