Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The National Renewable Energy Laboratory (NREL) Python panel-segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images [1]. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a faster-region-based convolutional neural network Resnet-50 feature pyramid network model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the panel-segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery [2]. The complete image dataset with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area [3].