Abstract
The Panel-Segmentation package is a computer vision (CV) framework for automated metadata extraction for solar PV arrays using satellite imagery. With Panel-Segmentation, a user inputs a set of latitude-longitude coordinates to automatically generate a satellite image using the Google Maps API, and runs the image through a deep learning framework to get a pixel-by-pixel representation of the solar panel(s) in an image. If a solar array is located, the user can then perform spectral clustering to cluster individual solar arrays in an image (if applicable), and run each cluster through an azimuth estimation algorithm. This package framework is versatile, allowing for future CV techniques to be added and/or modified.
- Developers:
-
Edun, Ayobami [1] ; Perry, Kirsten [1] ; Deline, Christopher [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2020-11-30
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies OfficePrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 49010
- Site Accession Number:
- SWR-21-18
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Edun, Ayobami, Perry, Kirsten, and Deline, Christopher.
Panel-Segmentation.
Computer Software.
https://github.com/NREL/Panel-Segmentation.
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office.
30 Nov. 2020.
Web.
doi:10.11578/dc.20201130.12.
Edun, Ayobami, Perry, Kirsten, & Deline, Christopher.
(2020, November 30).
Panel-Segmentation.
[Computer software].
https://github.com/NREL/Panel-Segmentation.
https://doi.org/10.11578/dc.20201130.12.
Edun, Ayobami, Perry, Kirsten, and Deline, Christopher.
"Panel-Segmentation." Computer software.
November 30, 2020.
https://github.com/NREL/Panel-Segmentation.
https://doi.org/10.11578/dc.20201130.12.
@misc{
doecode_49010,
title = {Panel-Segmentation},
author = {Edun, Ayobami and Perry, Kirsten and Deline, Christopher},
abstractNote = {The Panel-Segmentation package is a computer vision (CV) framework for automated metadata extraction for solar PV arrays using satellite imagery. With Panel-Segmentation, a user inputs a set of latitude-longitude coordinates to automatically generate a satellite image using the Google Maps API, and runs the image through a deep learning framework to get a pixel-by-pixel representation of the solar panel(s) in an image. If a solar array is located, the user can then perform spectral clustering to cluster individual solar arrays in an image (if applicable), and run each cluster through an azimuth estimation algorithm. This package framework is versatile, allowing for future CV techniques to be added and/or modified. },
doi = {10.11578/dc.20201130.12},
url = {https://doi.org/10.11578/dc.20201130.12},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20201130.12}},
year = {2020},
month = {nov}
}