Million solar roofs plan update#
Plans also are to update the map annually and add other countries and regions of the world. The team plans to add features to calculate a solar installation angle and orientation, which could accurately estimate its power generation. Combining satellite imagery and deep learning, we aimed to develop a framework to automatically construct, maintain, and update the solar installation database and realize the next-level visibility on renewable energy deployment,” the team said. “The recent breakthroughs of deep learning enables automatic and accurate image classification and segmentation. The model is the culmination of many ideas developed by multiple researchers over the years,” Google states. Google’s Inception V3 is “a widely-used image recognition model that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. Leveraging its high accuracy and scalability, we constructed a comprehensive high-fidelity solar deployment database for the contiguous United States, the authors say. “We developed DeepSolar, a deep learning framework analyzing satellite imagery to identify the GPS locations and sizes of solar photovoltaic panels.
Funding for the project included a State Grid Fellowship from Stanford Energy’s Bits & Watts initiative, and a Stanford Interdisciplinary Graduate Fellowship. Work was also contributed by the Majumdar Group’s Stanford-based Magic Lab, and the Stanford Sustainable Systems Lab, in Palo Alto, CA. The DeepSolar team included Jiafan Yu, Zhecheng Wang, Arun Majumdar and Ram Rajagopal. The system was able to correctly identify 93% of solar rooftops, according to a Stanford report on the project. Then DeepSolar learned to identify features associated with solar panels including roof color, texture, and size. They published the method and results of their study in Joule, Volume 2, Issue 12, 19 December 2018.ĭeepSolar learned to identify solar panels by analyzing some 370,000 images representing areas of 100 feet by 100 feet. “We built a nearly complete solar installation database for the contiguous United States utilizing a novel deep learning model applied to satellite imagery,” the researchers say. A total of 1.47 million solar rooftops were identified in the lower 48 states in the study.
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