Greenhouse photovoltaic panel detection

Can we detect photovoltaic installations on Earth?

Kruitwagen et al. now report another leap for the technique, with their analysis of 72.1 million square kilometres of Earth’s surface to detect commercial-, industrial- and utility-scale photovoltaic installations around the world.

Can remote-sensing be used to detect photovoltaic installations?

Many governments do not maintain a central database listing the size and locations of their country’s photovoltaic systems — installations of solar cells that generate electricity. Remote-sensing approaches using machine-learning techniques have the potential to collect these data by detecting such facilities in satellite images.

Can solar photovoltaic power generation be mapped using open satellite data?

Author to whom correspondence should be addressed. Solar photovoltaic (PV) power generation is a vital renewable energy to achieve carbon neutrality. Previous studies which explored mapping PV using open satellite data mainly focus in remote areas. However, the complexity of land cover types can bring much difficulty in PV identification.

Can remote sensing detect PV installation?

Remote sensing can play an important role in detecting PV installation. Conventional methods, including household surveys and utility interconnection filings, are limited in their completeness and spatial resolution in collecting the distribution of PV plants .

Can machine learning identify photovoltaic installations in high-resolution aerial and satellite imagery?

Machine-learning approaches for identifying photovoltaic installations in high-resolution aerial and satellite imagery have grown at an impressive speed. The method was first proposed 4 – 6 in 2016 — for example, as a way of finding residential installations in an area of 135 square kilometres across Fresno, California 4.

Should imaging spectroscopy data be used for PV identification?

This study aims to create greater awareness of the potential importance of imaging spectroscopy data for PV identification. As a physics-based approach, it is robust, transferable, and can provide data on PV coverage on a regional or global scale in short time.