https://orcid.org/0000-0002-5263-6225
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ABSTRACT
The constant need for decarbonization has led to the replacement of artificial light at night (ALAN) with light-emitting diodes (LEDs), inducing blue light pollution and its consequent adverse effects. As a result, there is an urgent need for the development of a technique for the rapid, accurate, and large-scale discrimination of the various illumination sources. The newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) can play this role by supplementing the existing nighttime light data with multispectral and high-resolution features. Along these lines, in this work, a novel approach to identify various types of illumination sources using machine learning in SDGSAT-1 images was proposed, taking Beijing as a worked example. The results indicate that: (1) The method can effectively distinguish the various types of light sources with an overall accuracy of 0.92 for ALAN and 0.95 for streetlights. (2) The illumination patterns can be clearly depicted, indicating distinct spatial heterogeneity in ALAN along Beijing’s 5th Ring Road. (3) Statistically significant disparities between road classes and streetlight types were detected, with a notable increase in LED streetlight usage as the road class diminishes. This work emphasizes the crucial role of SDGSAT-1 in analysing ALAN, providing valuable insights in urban lighting management.
Acknowledgments
We thank the anonymous reviewers and the editors for constructive comments that helped improve the manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.