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We consider the problem of automatically detecting small-scale solarphotovoltaic arrays for behind-the-meter energy resource assessment in highresolution aerial imagery. Such algorithms offer a faster and morecost-effective solution to collecting information on distributed solarphotovoltaic (PV) arrays, such as their location, capacity, and generatedenergy. The surface area of PV arrays, a characteristic which can be estimatedfrom aerial imagery, provides an important proxy for array capacity and energygeneration. In this work, we employ a state-of-the-art convolutional neuralnetwork architecture, called SegNet (Badrinarayanan et. al., 2015), tosemantically segment (or map) PV arrays in aerial imagery. This builds onprevious work focused on identifying the locations of PV arrays, as opposed totheir specific shapes and sizes. We measure the ability of our SegNetimplementation to estimate the surface area of PV arrays on a large, publiclyavailable, dataset that has been employed in several previous studies. Theresults indicate that the SegNet model yields substantial performanceimprovements with respect to estimating shape and size as compared to arecently proposed convolutional neural network PV detection algorithm.

Application of a semantic segmentation convolutional neural network for  accurate automatic detection and mapping of solar photovoltaic arrays in  aerial imagery