Urban public lighting classification method and analysis of energy and environmental effects based on SDGSAT-1 glimmer imager data

Abstract

Urban lighting system is an important part of urban space service functions. With the continuous advancement of sustainable development goals, governments of various countries are gradually paying attention to the energy, social, economic and environmental benefits of urban lighting systems, and promote the use of LED lighting to replace HPS lamp lighting to save urban energy consumption. In this study, we used the glimmer imager data of SDGSAT-1 launched in 2021, combined with the streetlight dataset of the District of Columbia, to establish a classification model for distinguishing LED lights from HPS lamps, with an overall recognition accuracy of 80.3% and the kappa coefficient is 0.603. Based on this model, we identify light types in the central cities of China’s three urban agglomerations: Beijing, Shanghai, Guangzhou, and Shenzhen, and describe their lighting composition and spatial distribution patterns. Additionally, we make a preliminary estimate of the money and electricity consumption saved by lighting renovations, and analyzed the impact of urban lighting changes on public perception. This study combines SDGSAT-1 data to propose a training model in urban lighting classification and applies it to a wide range of urban, the results of which help to provide data to support the implementation of SDG7.