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arXiv · 2107.10894

Power Plant Classification from Remote Imaging with Deep Learning

Michael Mommert·Linus Scheibenreif·Joëlle Hanna·Damian Borth
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Abstract

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.

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Michael Mommert, Linus Scheibenreif, Joëlle Hanna, Damian Borth. 2021-07-22. Power Plant Classification from Remote Imaging with Deep Learning. https://arxiv.org/abs/2107.10894

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