SolarNet: A Deep Learning Framework to Map Solar Power Plants In China From Satellite Imagery

TL;DR

SolarNet is a deep learning framework that accurately maps solar power plants across China using satellite imagery, providing valuable insights for stakeholders and demonstrating the potential of AI in renewable energy analysis.

Contribution

Introduces SolarNet, a novel deep learning-based semantic segmentation method for large-scale satellite imagery to detect and map solar farms in China.

Findings

Mapped 439 solar farms covering 2000 sq km in China

First application of deep learning for solar farm mapping in China

Provides detailed spatial data for energy stakeholders

Abstract

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.