Deep machine learning to identify PV and solar thermal systems in aerial images


David Lingfors from the Division of Civil Engineering and Built Environment, has co-authored an article on the identification of PV and solar thermal panels using machine learning, which has gained attention in the industry press.

The article describes how models trained in other countries encountered difficulties in sparsely populated areas, but they could be trained on Swedish conditions and achieve an accuracy of 95% in detecting installed solar energy systems.

Read the article in PV Magazine.

Read “Mapping of decentralised photovoltaic and solar thermal systems by remote sensing aerial imagery and deep machine learning for statistic generation


Last modified: 2022-02-08