  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.,
counties, cities, or even smaller 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 [1] aerial imagery, with over 2,700 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.
