This paper examines the application of deep learning methods for the automatic segmentation of satellite images with the aim of classifying land cover types based on their degree of vegetation cover. Three-channel satellite images and vector polygons annotated by experts were used as input data, including classes such as fields, fallow land, various types of tree and shrub vegetation, water bodies, and man-made objects.
