This thesis contains a study of automatic detection of large-scale solar plants from satellite imagery (Sentinel-1 and Sentinel-2) using geospatial cloud-based platform – Google Earth Engine. The thesis investigates to what extent and with what accuracy is possible to detect large-scale solar plants using specific remote sensing data. The study is connected with the existing research and approach and tries to provide a more accessible way using available processing tools. The approach and process are commented using coding samples, workflow scheme, and the results are compared using different machine learning classifiers. The analysis is conducted using Denmark as a study area with known ground-truth data, but the approach is ultimately transferred for detecting large-scale solar plants in the selected Germany state.