Power Plant Classification from Remote Imaging with Deep Learning

TL;DR

This paper demonstrates that deep learning models can accurately classify various power plant types and cooling mechanisms from Sentinel-2 satellite images, enabling global industrial site analysis.

Contribution

It introduces a deep learning approach using ResNet-50 to classify power plants and cooling methods from medium-resolution satellite imagery, achieving high accuracy.

Findings

Achieved 90.0% accuracy in classifying 10 power plant types.

Achieved 87.5% accuracy in identifying cooling mechanisms.

Proves feasibility of global industrial site classification from freely available satellite data.

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.