  Satellite remote imaging enables the detailed study of land use patterns on a
global scale. We investigate the possibility to improve the information content
of traditional land use classification by identifying the nature of industrial
sites from medium-resolution remote sensing images. In this work, we focus on
classifying different types of power plants from Sentinel-2 imaging data. Using
a ResNet-50 deep learning model, we are able to achieve a mean accuracy of
90.0% in distinguishing 10 different power plant types and a background class.
Furthermore, we are able to identify the cooling mechanisms utilized in thermal
power plants with a mean accuracy of 87.5%. Our results enable us to
qualitatively investigate the energy mix from Sentinel-2 imaging data, and
prove the feasibility to classify industrial sites on a global scale from
freely available satellite imagery.
