Ergonomic analysis of forest cover disturbance mapping methods using neural networks

Finding an effective neural network architecture takes a lot of time and requires significant computational resources. The goal of the article is to establish a relationship between the architecture features and its output characteristics so that, even before training the model, we can separate high-performance architectures from suboptimal ones. The article analyzes how the neural network architecture affects the model characteristics: training time, processing speed, and model size. The experiments have been conducted on a model based on the U-Net architecture. The main model characteristics have been changed: depth, width (average filter size), size of convolution kernels, and the presence or absence of skip connections. The problem of semantic segmentation of forest cover violations in a data set with satellite images has been solved. The established dependencies should facilitate the process of finding an effective neural network architecture for the semantic segmentation task.