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.
DeepSolar also learned adoption was higher in locations of increasing average household income.
From PBS NewsHour
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