  We consider the problem of automatically detecting small-scale solar
photovoltaic arrays for behind-the-meter energy resource assessment in high
resolution aerial imagery. Such algorithms offer a faster and more
cost-effective solution to collecting information on distributed solar
photovoltaic (PV) arrays, such as their location, capacity, and generated
energy. The surface area of PV arrays, a characteristic which can be estimated
from aerial imagery, provides an important proxy for array capacity and energy
generation. In this work, we employ a state-of-the-art convolutional neural
network architecture, called SegNet (Badrinarayanan et. al., 2015), to
semantically segment (or map) PV arrays in aerial imagery. This builds on
previous work focused on identifying the locations of PV arrays, as opposed to
their specific shapes and sizes. We measure the ability of our SegNet
implementation to estimate the surface area of PV arrays on a large, publicly
available, dataset that has been employed in several previous studies. The
results indicate that the SegNet model yields substantial performance
improvements with respect to estimating shape and size as compared to a
recently proposed convolutional neural network PV detection algorithm.
