How AI Spotted Every Solar Panel in the U.S.

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.