Deep Learning and Climate Change

Introduction

As a pioneer in the fight against global climate change, Germany is investing more and more in renewable energy, especially in wind energy. With about 300 new turbines from 2016 to 2017, North Rhine Westphalia is one of the major federal states in the construction of new wind turbines. To assess the potential of wind power and the planning of new turbines, it is essential to track the spatial location of wind turbines along with their type and to combine them with information on the characteristics of average wind speed.

In the present case of use, a methodology is described that can locate and segment wind turbines in satellite images. The implemented neural network architecture is called U-Net and is the leading standard for image segmentation. The output is a pixel-level prediction of the likelihood of a pixel belonging to a wind turbine. The deep learning structure was trained to predict wind turbine polygons in 280,000 satellite images covering the entire region of North Rhine-Westphalia. The output has been transferred to the ArcGIS Geographic Information System and can be accessed online through multiple devices. The wind turbine layer can be used for comprehensive analysis of wind power potentials and spatial planning of turbines.

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