###### Abstract  
This paper proposes an automatic air-to-ground (A2G) channel model selection method based on machine learning (ML) using digital surface model (DSM) terrain data. In order to verify whether a communication network for a new non-terrestrial user service such as Urban Air Mobility (UAM) satisfies the required performance, it is necessary to perform a simulation reflecting the characteristics of the corresponding terrain environments as accurately as possible. For this simulation, A2G channel models corresponding to various terrain environments and a method of automatically classifying the terrain type of the simulation area must be provided. Many A2G channel models based on actual measurement results exist, but the practical automatic topography classification method still needs to be developed. This paper proposes the first practical automatic topography classification method using a two-step neural network-based classifier utilizing various geographic feature data as input. Since there is no open topography dataset to evaluate the accuracy of the proposed method, we built a new dataset for five topography classes that reflect the characteristics of Korea's topography, which is also a contribution of our study. The simulation results using the new data set show that the proposed ML-based method could increase the selection accuracy compared to the technique for direct classification by humans or the existing cross-correlation-based classification method. Since the proposed method utilizes the DSM data, open to the public, it can easily reflect the different terrain characteristics of each country. Therefore, the proposed method can be effectively used in the realistic performance evaluation of new non-terrestrial communication networks utilizing vast airspace such as UAM or 6G mobile communications. G 2022 20  
Project (3 GPP) is actively carrying out standardization activities for efficient coverage expansion of three-dimensional airspace [6; 7; 8; 9]. In order to extend the coverage of the cellular network to the sky, there are many technical issues, such as uplink and downlink interference management [6; 7] and identification of users in the air [7]. The impact on the system structure of the 5G network for UAV identification and control [8] and the application of the 5G network for UTM application is also studied [9]. For research and standardization of new communication systems in the airspace, it is essential to simulate communication performance using a channel model that reflects the actual propagation characteristics of signals between UAVs and ground stations. Therefore, various efforts have been made to study the channel model based on the air-to-ground (A2G) measurement results in various terrain environments. The selection of appropriate channel measurement parameters in each environment is vital to obtain accurate channel statistics. The A2G propagation environment is generally classified into flat land, hills, mountains, and water surfaces, and in particular, the ground has additional characteristics such as grass, forests, and buildings. In this regard, the International Telecommunication Union (ITU) classified the popular topography classes and provided them as a list [10]. Based on this, in [11], the measurement environment and A2G propagation scenario were organized by the topographical environments, and the difference in channel characteristics according to the different terrains was specified. Various studies have been conducted to develop A2G channel models in several types of topography, such as water surfaces [12; 13; 14], mountains [15], cities [16; 17], and suburbs [16], based on actual measurement results using airplanes or UAVs. As a result, parameters such as pass loss, delay spread, K-factor, and the first and second-order statistics in the various frequency bands are different for each topography class. Based on the measurement results, a channel model that can be used in the simulation can be created.