  Renewable energy such as solar power is critical to fight the ever more
serious climate change. China is the world leading installer of solar panel and
numerous solar power plants were built. In this paper, we proposed a deep
learning framework named SolarNet which is designed to perform semantic
segmentation on large scale satellite imagery data to detect solar farms.
SolarNet has successfully mapped 439 solar farms in China, covering near 2000
square kilometers, equivalent to the size of whole Shenzhen city or two and a
half of New York city. To the best of our knowledge, it is the first time that
we used deep learning to reveal the locations and sizes of solar farms in
China, which could provide insights for solar power companies, market analysts
and the government.
