NEWS AND VIEWS
27 October 2021
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
Lynn H. Kaack is in the Data Science Lab, Hertie School, 10117 Berlin, Germany.
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
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Source: https://www.nature.com/articles/d41586-021-02875-y
