Unsupervised classification of satellite images is the process of grouping similar pixels on an image into homogeneous clusters based primarily on their spectral characteristics. This approach does not require reference (labeled) data, unlike supervised classification, therefore it can be used as a method of first choice. Satellite image classification is commonly used in a variety of fields, including environmental monitoring, land cover mapping, and disaster management. The generated thematic maps can be used to identify and monitor changes in land use, and assess the impact of natural disasters. During this workshop, participants gained practical knowledge and skills to perform unsupervised classification of Landsat data using the R language. It was demonstrated step by step how to use and prepare raster data for analysis. Popular grouping methods was discussed, as well as the preparation of a land cover map with interpretation of the results. The workshop also covered the challenges and limitations of unsupervised classification, such as subjective interpretation of results difficulty of selecting the optimal number of clusters, and validation methods for ensuring the accuracy and reliability of results. The workshop is aimed at beginners, but basic knowledge of GIS and satellite remote sensing is required.