Solar power is a clean and sustainable energy source, but scaling its generation is essential to reduce dependence on conventional energy. This is being achieved through the development of large-scale solar photovoltaic (PV) parks—some covering thousands of acres and housing millions of solar panels. Accurate information on the location and extent of these parks is valuable for governments, policymakers, international organizations, researchers, and private enterprises.

Traditional methods of identifying and mapping solar PV parks, such as field surveys and manual interpretation, are time-consuming, costly, and prone to errors. This deep learning model automates the detection and mapping of solar PV parks from imagery, generating classified maps that show the spatial distribution and concentration of installations. It significantly reduces the time and effort required for monitoring and analysis while improving accuracy and scalability.