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Automatic detection of solar photovoltaic arrays in high resolution aerial imagery

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  • Malof, Jordan M.
  • Bradbury, Kyle
  • Collins, Leslie M.
  • Newell, Richard G.

Abstract

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 (135km2) 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.

Suggested Citation

  • Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:229-240
    DOI: 10.1016/j.apenergy.2016.08.191
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    13. Hu, Wei & Bradbury, Kyle & Malof, Jordan M. & Li, Boning & Huang, Bohao & Streltsov, Artem & Sydny Fujita, K. & Hoen, Ben, 2022. "What you get is not always what you see—pitfalls in solar array assessment using overhead imagery," Applied Energy, Elsevier, vol. 327(C).
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    15. Mayer, Kevin & Rausch, Benjamin & Arlt, Marie-Louise & Gust, Gunther & Wang, Zhecheng & Neumann, Dirk & Rajagopal, Ram, 2022. "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D," Applied Energy, Elsevier, vol. 310(C).
    16. Juan-Pablo Villegas-Ceballos & Mateo Rico-Garcia & Carlos Andres Ramos-Paja, 2022. "Dataset for Detecting the Electrical Behavior of Photovoltaic Panels from RGB Images," Data, MDPI, vol. 7(6), pages 1-12, June.
    17. Edun, Ayobami S. & Perry, Kirsten & Harley, Joel B. & Deline, Chris, 2021. "Unsupervised azimuth estimation of solar arrays in low-resolution satellite imagery through semantic segmentation and Hough transform," Applied Energy, Elsevier, vol. 298(C).
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