n some states, solar energy accounts for upwards of 10 percent of total electricity generation. It’s definitely a source of power that’s on the rise, whether it be to lessen our dependence on fossil fuels, nuclear power, or the energy grid, or simply to take advantage of the low costs. This form of energy, however, is highly decentralized, so it’s tough to know how much solar energy is being extracted, where, and by whom.
The system developed by Rajagopal, along with his colleagues Jiafan Yu and Zhecheng Wang, is called DeepSolar, and it’s an automated process whereby hi-res satellite photos are analyzed by an algorithm driven by machine learning. DeepSolar can identify solar panels, register their locations, and calculate their size. The system identified 1.47 million individual solar installations across the United States, whether they be small rooftop configurations, solar farms, or utility-scale systems. This exceeds the previous estimate of 1.02 million installations. The researchers have made this data available at an open-source website.
By using this new approach, the researchers were able to accurately scan billions of tiles of high-resolution satellite imagery covering the continental U.S., allowing them to classify and measure the size of solar systems in a few weeks rather than years, as per previous methods. Importantly, DeepSolar requires minimal human supervision.
“The algorithm breaks satellite images into tiles. Each tile is processed by a deep neural net to produce a classification for each pixel in a tile. These classifications are combined together to detect if a system—or part of—is present in the tile,” Rajagopal told Gizmodo.
The neural net can then determine which tile is a solar panel, and which is not. The network architecture is such that after training, the layers of the network produce an activation map, also known as a heat map, that outlines the panels. This can be used to obtain the size of each solar panel system.