Automatic Detection and Mapping of Solar Photovoltaic Arrays with Deep Convolutional Neural Networks in High Resolution Satellite Images
Kaiji He, Long Zhang
Abstract
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 n umber 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.