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

Conference · Fri Aug 26 04:00:00 EDT 2022

OSTI ID:1885131

Perry, Kirsten; Campos, Christopher

The 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. 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-RCNN Resnet-50 feature pyramid network (FPN) 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 (mAP) 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. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1885131
• Report Number(s): NREL/PR-5K00-83199; MainId:83972; UUID:4c16019f-7b62-4b10-bdd4-608a54201791; MainAdminID:65251
• Country of Publication: United States
• Language: English

Related Subjects

SOLAR ENERGY
deep learning
metadata
photovoltaics
satellite imagery
solar