Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques

Kai Mainzer*, Sven Killinger, Russell McKenna, Wolf Fichtner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)


The local generation of renewable electricity through roof-mounted photovoltaic (PV) systems on buildings in urban areas provides huge potentials for the mitigation of greenhouse gas emissions. This contribution presents a new method to provide local decision makers with tools to assess the remaining PV potential within their respective communities. It allows highly detailed analyses without having to rely on 3D city models, which are often not available. This is achieved by a combination of publicly available geographical building data and aerial images that are analyzed using image recognition and machine learning approaches. The method also employs sophisticated algorithms for irradiance simulation and power generation that exhibit a higher accuracy than most existing PV potential studies. The method is demonstrated with an application to the city of Freiburg, for which a technical PV electricity generation potential of about 524 GWh/a is identified. A validation with a 3D city model shows that the correct roof azimuth can be determined with an accuracy of about 70% and existing solar installations can be detected with an accuracy of about 90%. This demonstrates that the method can be employed for spatially and temporally detailed PV potential assessments in arbitrary urban areas when only public geographical building data is available instead of exact 3D city model data. Future work will focus on methodological improvements as well as on the integration of the method within an urban energy system modeling framework.

Original languageEnglish
Pages (from-to)561-573
Number of pages13
JournalSolar Energy
Early online date3 Jul 2016
Publication statusPublished - Oct 2017

Bibliographical note

The authors gratefully acknowledge the financial support of the Federal Ministry of Education and Research (BMBF – Germany) for the project Wettbewerb Energieeffiziente Stadt (03SF0415B) and the Nagelschneider Foundation. The authors would also like to thank David Schlund for his contributions to earlier versions of this method.


  • Image recognition
  • Machine learning
  • Module orientation
  • PV potential


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