Support vector machine based light pollution risk evaluation in Shanghai

Weihao Sun

doi:10.1117/12.3010427

15 November 2023 Support vector machine based light pollution risk evaluation in Shanghai

Weihao Sun

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 1281519 (2023) https://doi.org/10.1117/12.3010427
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

With the rapid development of China's economy, cities are becoming more and more prosperous, and many cities are lit up at night. However, when urban lighting exceeds a certain limit and affects the life of residents, it becomes light pollution. In order to monitor and prevent light pollution, we first defined three kinds of light pollution accurately, and constructed a light pollution evaluation model for Shanghai based on ground-based satellite remote sensing technology and urban light sampling data, using the advantages of support vector machine model robustness, can effectively avoid "dimensional disaster", and simplify the multi-classification problem. The results showed that the distribution of nighttime illumination in Shanghai ranges from 0 to 60 lumens, with great variability from region to region, and the illumination level can reach 35 lumens or more in some important places such as large shopping malls and large squares, which require light pollution prevention. This study can realize the light pollution degree detection, provide data support and reference for reducing the impact of light pollution, and meet the demand of night lighting for scientific and sustainable development of the city.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weihao Sun "Support vector machine based light pollution risk evaluation in Shanghai", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 1281519 (15 November 2023); https://doi.org/10.1117/12.3010427

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