STANFORD, Calif., Dec. 28, 2018 — Stanford University scientists developed a machine learning program that analyzed more than 1 billion high-resolution satellite images and identified nearly every photovoltaic solar power installation in the contiguous 48 U.S. states. They found 1.47 million installations, more than previous estimates. Their results, which include activators and impediments to solar deployment, are publicly available on the project’s website.
The team provided its machine learning program, named DeepSolar, with about 370,000 images. Each image was labeled as either having or not having a solar panel present. From these images, DeepSolar learned to identify features associated with solar panels — for example, color, texture, and size. “All of these need to be learned by the machine,” said researcher Jiafan Yu. DeepSolar learned to identify images containing solar panels with 93 percent accuracy. [click for full article]