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How deep learning helped to map every solar panel in the US

Deep learning has been used to identify 1.47 million solar installations across the United States, exceeding the latest estimate of 1.02 million.

What’s new: Solar panels are becoming increasingly popular across the US, but it’s proved difficult to pinpoint their exact number. Researchers from Stanford University have got us much closer, thanks to a new system called DeepSolar, which uses deep learning to scan satellite images for solar panels.

How it worked: The team trained DeepSolar on 370,000 satellite images by teaching it which ones included solar panels. The program then worked out how to spot solar panels, finding them correctly 93% of the time. It took about a month for the system to scan the billion images needed to reach its final figure.

Uses: The maps could help us better understand the take-up of solar in the US. Looking ahead, the researchers plan to use their system to create solar maps for other countries, and perhaps deploy it to locate wind turbines and other energy infrastructure.
