Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
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© 2016 Elsevier Ltd The quantity of small scale solar photovoltaic (PV) arrays in the United States has grown rapidly in recent years. As a result, there is substantial interest in high quality information about the quantity, power capacity, and energy generated by such arrays, including at a high spatial resolution (e.g., cities, counties, or other small regions). Unfortunately, existing methods for obtaining this information, such as surveys and utility interconnection filings, are limited in their completeness and spatial resolution. This work presents a computer algorithm that automatically detects PV panels using very high resolution color satellite imagery. The approach potentially offers a fast, scalable method for obtaining accurate information on PV array location and size, and at much higher spatial resolutions than are currently available. The method is validated using a very large (135 km 2 ) collection of publicly available (Bradbury et al., 2016) aerial imagery, with over 2700 human annotated PV array locations. The results demonstrate the algorithm is highly effective on a per-pixel basis. It is likewise effective at object-level PV array detection, but with significant potential for improvement in estimating the precise shape/size of the PV arrays. These results are the first of their kind for the detection of solar PV in aerial imagery, demonstrating the feasibility of the approach and establishing a baseline performance for future investigations.
Published Version (Please cite this version)10.1016/j.apenergy.2016.08.191
Publication InfoBradbury, Kyle; Collins, Leslie; Malof, Jordan; & Newell, Richard G (2016). Automatic detection of solar photovoltaic arrays in high resolution aerial imagery. Applied Energy, 183. 10.1016/j.apenergy.2016.08.191. Retrieved from https://hdl.handle.net/10161/16754.
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Professor of Electrical and Computer Engineering
Leslie M. Collins earned the BSEE degree from the University of Kentucky, and the MSEE, and PhD degrees from the University of Michigan, Ann Arbor. From 1986 through 1990 she was a Senior Engineer at Westinghouse Research and Development Center in Pittsburgh, PA. She joined Duke in 1995 as an Assistant Professor and was promoted to Associate Professor in 2002 and to Professor in 2007. Her research interests include physics-based statistical signal processing, subsurface sensing, auditory prosthe
Assistant Research Professor in the Department of Electrical and Computer Engineering
My research involves the application of advanced machine learning and computer vision techniques to real-world problems. Recently, I have used deep learning techniques to automatically recognize objects in aerial imagery.
Dr. Richard G. Newell is the President and CEO of Resources for the Future (RFF), an independent, nonprofit research institution that improves environmental, energy, and natural resource decisions through impartial economic research and policy engagement. From 2009 to 2011, he served as the administrator of the US Energy Information Administration, the agency responsible for official US government energy statistics and analysis. Dr. Newell is an adjunct professor at Duke University, where he
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