We present a novel method for detecting solar panels and its geometry on aerial imagery. The goal is to know the exact locations, dimensions and potential of every solar installation in Switzerland.
The renewable energy statistics estimate the production of solar power and solar heat. To validate these statistics, the existing installations are to be identified and quantified fully automatically using deep learning algorithms from aerial photographs. Thanks to these methods, the current and future potential for use in Switzerland can be determined more precisely. Above all, this will also allow the status of implementation of the Energy Strategy 2050 of Switzerland to be determined.
To train the model various solar panels have been collected using crowd sourcing. A Flask-based Web-Service was created to collect data.
To create the model a GPU Cluster using 4 NVidia V100 was used. It is shown how this can be accomplished.
Big Data, Computer Vision, Deep Learning, Data Science, Machine Learning, Visualisation
Domain Expertise:some
Python Skill Level:basic
Abstract as a tweet:Detecting Solar Panels from aerial imagery using #Python #DeepLearning #CrowdSourcing