Alleviate light pollution by recognizing urban night-time light control area based on computer vision techniques and remote sensing imagery

Abstract

Light pollution caused by unreasonable and excessive use of outdoor artificial light at night (ALAN) has become one of the fastest growing and most urgent global environmental problems. Therefore, the International Dark-Sky Association advocated the more controlled use of ALAN in urban area to protect night-time environment and enable sustainable development. However, relevant research and lighting planning are still in the enlightenment stage. This study focused on a typical city experiencing significant ALAN growth, combining night-time remote sensing imagery, urban big data and other muti-source heterogeneous data to systematically research urban night-time light status. We first analyzed the spatio-temporal evolution pattern of ALAN. Subsequently, advanced computer vision algorithms were proposed to recognize the night-time light control area (NLCA) based on the perspective of supply and demand balance. Results suggest both the ALAN intensity and area have grown rapidly over the past decade in the study area, with the intensity increasing by 82 % and the area expanding by 42 %. And a 108.5-square-kilometre area of the NLCA was intelligently delineated, for where the ALAN intensity was more than the demand of population, indicating that the management of the night-time light environment an urgent matter. Therefore, this study proposed practical management solutions according to the different functional zones in city within the NLCA, for further promoting the green transformation and smart upgrading of urban outdoor lighting. To sum up, our research offers a novel approach to resolving the issue of urban light pollution by recognizing the NLCA, as well as facilitating energy conservation and emission reduction.