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

arXiv: https://arxiv.org/abs/1912.03685 · HTML: https://mlsanity.com/paper/1912.03685/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis](https://mlsanity.com/paper/2202.12939/) (2022)
- [Solar Panel Mapping via Oriented Object Detection](https://mlsanity.com/paper/2502.03592/) (2025)
- [Predicting Levels of Household Electricity Consumption in Low-Access Settings](https://mlsanity.com/paper/2112.08497/) (2021)
- [Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery](https://mlsanity.com/paper/2608.10801/) (2026)

## Abstract (arXiv)

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.