There are at least 1.47 million solar installations of varying sizes in the 48 contiguous U.S. states, from home rooftop panels to utility-owned solar power plants. That's the conclusion of DeepSolar, a machine learning algorithm developed by researchers at Stanford University that searches satellite images for solar panels. The count is higher than some previous estimates, like the OpenPV project's count of 1.02 million installations. The researchers, led by Ram Rajagopal, associate professor of civil and environmental engineering, and Arun Majumdar, professor of mechanical engineering, trained DeepSolar on a set of 370,000 satellite images, each covering a region measuring approximately 9 square meters (100 square feet), by indicating which ones included solar panels. The machine-learning program then figured out how to identify solar panels, spotting them correctly 93 percent of the time.
Researchers at Stanford University engineers used a deep learning computer model to identify every solar panel in the continuous U.S. from satellite images. Stanford University engineers have developed a method for locating every solar panel in the contiguous U.S. from a massive satellite image database via a deep learning computer model. The researchers used a pre-trained model called Inception as the basis for the DeepSolar neural network's clustering and classifying of pixels in images. DeepSolar scanned more than 1 billion image "tiles," comprising areas bigger than a neighborhood but smaller than a zip code; each tile had 102,400 pixels, and DeepSolar classified each pixel in each tile, determining whether it was likely part of a solar panel or not. The network completed this task in less than a month, ascertaining that regions with more sun exposure had greater solar panel adoption than areas with less average sunlight.
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. 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.
It would be impractical to count the number of solar panels in the US by hand, and that makes it difficult to gauge just how far the technology has really spread. Stanford researchers have a solution: make AI do the heavy lifting. They've crafted a deep learning system, DeepSolar, that mapped every visible solar panel in the US -- about 1.47 million of them, if you're wondering. The neural network-based approach turns satellite imagery into tiles, classifies every pixel within those tiles, and combines those pixels to determine if there are solar panels in a given area, whether they're large solar farms or individual rooftop installations. This method is accurate, requires only basic oversight and (most importantly) fast.
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