Machine learning enables global solar-panel detection - Nature.com

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• 27 October 2021

Machine learning enables global solar-panel detection

An inventory of the world’s solar-panel installations has been produced with the help of machine learning, revealing many more than had previously been recorded. The results will inform efforts to meet global targets for solar-energy use.

• Lynn H. Kaack  ORCID: http://orcid.org/0000-0003-3630-3102 ⁰

1. Lynn H. Kaack

   1. Lynn H. Kaack is in the Data Science Lab, Hertie School, 10117 Berlin, Germany.

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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. Writing in Nature, Kruitwagen et al.¹ show how machine learning can be used to mine imagery of the entire globe to produce an inventory of commercial-, industrial- and utility-scale solar installations. The authors locate more than 68,000 such facilities, many of which were not on record.

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Nature 598, 567-568 (2021)

doi: https://doi.org/10.1038/d41586-021-02875-y

References

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   Kruitwagen, L. et al. Nature 598, 604–610 (2021).

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Competing Interests

The author declares no competing interests.

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