## 1 Introduction
The model does not capture environment details, unlike that in [16], where features extracted from satellite images are included during model development to capture environmental details. [PERSON] et al. [17] also developed models for multiple frequencies, multiple antenna heights, and multiple environments. A clutter height feature was used in representing the various types of environments such that each type of environment had an integer value representing it. The problem with this method is that it does not take into consideration the micro differences within each type of environment, whereas [16] included satellite images to acknowledge the effects of these micro variations. The ResNet50V2 used in [16] takes time and memory space to train. This work proposes the use of a less complex CNN that will train faster and occupy less memory space. Another issue to note is the dependence of a CNN's accuracy on a large volume of data and a large amount of time it takes a CNN to get trained with the data, even though the prediction time is minimal. An alternative is to use hand-crafted feature extraction methods to deal with data limitation as well as the large training time. [PERSON] et al. used the Segmentation-based Fractal Texture Analysis (SFTA) texture extraction method to extract features from images with grayscale shadings of building heights between the transmitter and receiver and developed three models with features based on RF, Extreme Gradient Boosting Machine (XGBoost), and Light Gradient Boosting Machine (LightGBM) [18]. However, a comparison between the performance of SFTA and CNN was not performed. To the best of our knowledge, their work is the first that incorporated texture feature extraction in a path loss problem. Predictions from a LightGBM model were more accurate, although the hyper-parameter optimization that was carried out in this work was not carried out in theirs to ascertain the optimal hyper-parameters of the algorithms. In other domains, comparisons were made between the performance of hand-crafted features and CNN, in which the CNN-extracted features performed better [19; 20]. Combining CNN features and hand-crafted features improved the accuracy [21; 22]. The aim of this work is to develop a single ML-based path loss model valid for some network parameters (UHF band and antenna heights) and environments (rural, suburban, urban, and urban highrise) with the effect of terrain/elevation taken into consideration. The essence is to have the ML path loss models valid for various values of network parameters and environment types, similar to existing empirical models. The ability of ML algorithms to model non-linear relationships improves performance over the existing empirical models. Numeric features combined with satellite image features were used as input to the model. Satellite images are used to capture micro and macro variations within the environments, unlike in existing empirical models, where such variations are not captured, resulting in poor predictions in new environments. The performance of features extracted using various methods (hand-crafted and CNN) and their combinations on the model's output was evaluated. Hand-crafted features were investigated to ascertain the possibility of their use in this application, knowing that they need no training, unlike CNN, which requires training with a lot of data. However, since combining hand-crafted and CNN features was reported to improve accuracy in some applications, we also investigated it, and improvements in accuracy and training time were observed compared to [16]. In addition, the study explores feature importance and analysis. Feature importance was not conducted in all works cited in this paper, except in [17], where only non-image features were considered for model development. Our contributions are:  
* Review of path loss prediction based on image processing using CNN.