Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery
Abstract
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. 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. Additionally, 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. 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.
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- OSTI Identifier:
- 1957702
- Alternate Identifier(s):
- OSTI ID: 1915249; OSTI ID: 1957703
- Report Number(s):
- NREL/JA-5K00-82613
Journal ID: ISSN 2156-3381; 10008194
- Grant/Contract Number:
- AC36-08GO28308; 38258
- Resource Type:
- Published Article
- Journal Name:
- IEEE Journal of Photovoltaics
- Additional Journal Information:
- Journal Name: IEEE Journal of Photovoltaics Journal Volume: 13 Journal Issue: 2; Journal ID: ISSN 2156-3381
- Publisher:
- Institute of Electrical and Electronics Engineers
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 14 SOLAR ENERGY; deep learning; metadata extraction; satellite imagery; solar; Azimuth; metadata; mount; satellites; pipelines; photovoltaic systems; object detection; training
Citation Formats
Perry, Kirsten, and Campos, Christopher. Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery. United States: N. p., 2023.
Web. doi:10.1109/JPHOTOV.2022.3230565.
Perry, Kirsten, & Campos, Christopher. Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery. United States. https://doi.org/10.1109/JPHOTOV.2022.3230565
Perry, Kirsten, and Campos, Christopher. Wed .
"Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery". United States. https://doi.org/10.1109/JPHOTOV.2022.3230565.
@article{osti_1957702,
title = {Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery},
author = {Perry, Kirsten and Campos, Christopher},
abstractNote = {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. 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. Additionally, 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. 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.},
doi = {10.1109/JPHOTOV.2022.3230565},
journal = {IEEE Journal of Photovoltaics},
number = 2,
volume = 13,
place = {United States},
year = {Wed Mar 01 00:00:00 EST 2023},
month = {Wed Mar 01 00:00:00 EST 2023}
}
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https://doi.org/10.1109/JPHOTOV.2022.3230565
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