Just how many property owners have installed solar panels? No one knows the precise answer—yet.
But Stanford University scientists hope to change that using a combination of imagery from space and machine learning. According to TechCrunch:
Stanford engineers (mechanical and civil, respectively) Arun Majumdar and Ram Rajagopal decided to remedy this with what seems like, in retrospect, rather an obvious solution.
Machine learning systems are great at looking at images and finding objects they’ve been “trained” to recognize, whether it’s cats, faces, or cars… so why not solar panels?
Their team, including grad students Jiafan Yu and Zhecheng Wang, put together an image recognition machine learning agent trained on hundreds of thousands of satellite images. The model learns both to identify the presence of solar panels in an image, and to find the shape and area of those panels.
Their study shows more solar installations than expected. According to Stanford:
University scientists analyzed more than a billion high-resolution satellite images with a machine learning algorithm and identified nearly every solar power installation in the contiguous 48 states. The results are described in a paper published in the Dec. 19 issue of Joule. The data are publicly available on the project’s website.
The analysis found 1.47 million installations, which is a much higher figure than either of the two widely recognized estimates. The scientists also integrated U.S. Census and other data with their solar catalog to identify factors leading to solar power adoption.
“We can use recent advances in machine learning to know where all these assets are, which has been a huge question, and generate insights about where the grid is going and how we can help get it to a more beneficial place,” said Ram Rajagopal, associate professor of civil and environmental engineering, who supervised the project with Arun Majumdar, professor of mechanical engineering.