Skip to main content

Automatic solar photovoltaic panel detection in satellite imagery

Publication ,  Conference
Malof, JM; Hou, R; Collins, LM; Bradbury, K; Newell, R
Published in: 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015
January 1, 2015

The quantity of rooftop solar photovoltaic (PV) installations has grown rapidly in the US in recent years. There is a strong interest among decision makers in obtaining high quality information about rooftop PV, such as the locations, power capacity, and energy production of existing rooftop PV installations. Solar PV installations are typically connected directly to local power distribution grids, and therefore it is important for the reliable integration of solar energy to have information at high geospatial resolutions: by county, zip code, or even by neighborhood. Unfortunately, traditional means of obtaining this information, such as surveys and utility interconnection filings, are limited in availability and geospatial resolution. In this work a new approach is investigated where a computer vision algorithm is used to detect rooftop PV installations in high resolution color satellite imagery and aerial photography. It may then be possible to use the identified PV images to estimate power capacity and energy production for each array of panels, yielding a fast, scalable, and inexpensive method to obtain rooftop PV estimates for regions of any size. The aim of this work is to investigate the feasibility of the first step of the proposed approach: detecting rooftop PV in satellite imagery. Towards this goal, a collection of satellite rooftop images is used to develop and evaluate a detection algorithm. The results show excellent detection performance on the testing dataset and that, with further development, the proposed approach may be an effective solution for fast and scalable rooftop PV information collection.

Duke Scholars

Published In

2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015

DOI

ISBN

9781479999828

Publication Date

January 1, 2015

Start / End Page

1428 / 1431
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Malof, J. M., Hou, R., Collins, L. M., Bradbury, K., & Newell, R. (2015). Automatic solar photovoltaic panel detection in satellite imagery. In 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015 (pp. 1428–1431). https://doi.org/10.1109/ICRERA.2015.7418643
Malof, J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell. “Automatic solar photovoltaic panel detection in satellite imagery.” In 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015, 1428–31, 2015. https://doi.org/10.1109/ICRERA.2015.7418643.
Malof JM, Hou R, Collins LM, Bradbury K, Newell R. Automatic solar photovoltaic panel detection in satellite imagery. In: 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015. 2015. p. 1428–31.
Malof, J. M., et al. “Automatic solar photovoltaic panel detection in satellite imagery.” 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015, 2015, pp. 1428–31. Scopus, doi:10.1109/ICRERA.2015.7418643.
Malof JM, Hou R, Collins LM, Bradbury K, Newell R. Automatic solar photovoltaic panel detection in satellite imagery. 2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015. 2015. p. 1428–1431.

Published In

2015 International Conference on Renewable Energy Research and Applications, ICRERA 2015

DOI

ISBN

9781479999828

Publication Date

January 1, 2015

Start / End Page

1428 / 1431