Automatic detection and mapping of solar photovoltaic arrays with deep convolutional neural networks in high resolution satellite images

The locations and capacities of household rooftop
solar photovoltaic (PV) arrays are important for power grid
planning. However, it is hard to collect such information manually
as a significant number of PV arrays are distributed dispersedly
in residential areas. With the development of deep learning
model on image recognition, it brings an opportunity to build
an intelligent detector that is able to automatically identify and
delineate PV arrays in satellite images. Convolutional neural
networks (CNN) are ideally suited for this task as CNN has
capability of capturing spatial information of digital images by
convolution operation. In this work, we trained a deep CNN
with manually annotated satellite images taken from a region of
Manchester (UK), and validated our detector with another set
of satellite images taken from the same city. Our results indicate
that the detector is capable to identify PV arrays with a high
accuracy and delineate them in pixel-wise with high precision,
showing the feasibility of our approach.