Standford launches DeepSolar to locate all US solar panels
Standford University scientists has launched “DeepSolar”, a deep learning framework that have identified the GPS locations and sizes of almost all U.S. solar power installations from a billion images. This data will be enormously useful for managing the changing U.S. electricity system.
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. The resulting database contains not only residential solar installations, but those on the roofs of businesses, as well as many large, utility-owned solar power plants
“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,” says Ram Rajagopal, an associate professor of civil and environmental engineering who helped lead the project
What is DeepSolar Project?
DeepSolar is a deep learning framework that analyzes satellite imagery to identify the GPS locations and sizes of solar photovoltaic (PV) panels. DeepSolar constructed a comprehensive high-fidelity solar deployment database for the contiguous U.S.
Highlights of DeepSolar
- It is an accurate deep learning model for detecting solar panel on satellite imagery
- Built a nearly complete solar installation database for the contiguous US
- Identified key socioeconomic factors correlating with solar deployment density
- A predictive model to estimate solar deployment density at census tract level
You can read more about DeepSolar here at Stanford University page.