Estimation of rooftop solar energy generation using Satellite Image Segmentation

Rooftop solar energy potential has traditionally been estimated by surveying the number of large buildings in a given area. In this work, we propose a fast and low-cost method to estimate the rooftop photovoltaic solar energy generated in a particular area by utilizing satellite imagery - even though it may be of low resolution. We employ a deep learning based approach to carry out image segmentation on low resolution satellite images of Bangalore, India. Three different model architectures (U-Net, SegNet, FCN) were trained on manually hand-labelled data and tested for the task of semantic segmentation of satellite images. U-Net was found to yield the best results. By using a custom modified U-Net to segment the images, we arrive at the building rooftop area available for solar panels. To calculate the annual solar energy generated in gigawatt-hour (GWh), the incident solar insolation values from the U.S. National Renewable Energy Laboratory (NREL) based on observed weather patterns in Bangalore, and standard values from datasheets of photovoltaic manufacturers are used.