Automatic solar photovoltaic panel detection in satellite imagery

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Malof, JM; Hou, R; Collins, LM; Bradbury, K; Newell, R

Published Date

  • January 1, 2015

Published In

  • 2015 International Conference on Renewable Energy Research and Applications, Icrera 2015

Start / End Page

  • 1428 - 1431

International Standard Book Number 13 (ISBN-13)

  • 9781479999828

Digital Object Identifier (DOI)

  • 10.1109/ICRERA.2015.7418643

Citation Source

  • Scopus