Abstract
Too much sunlight exposure would cause heat stress for people during the hot summer, although a minimum amount of sunlight is required for humans. Unprotected exposure to ultraviolet (UV) radiation in the sunlight is one of the major risk factors for skin cancer. Mitigating the heat stress and UV exposure caused by too much sunlight exposure becomes a pressing issue in the context of increasing temperature in urban areas. In this study, we propose an individualized and short-term effective strategy to reduce sunlight exposure for urban residents. We developed a routing algorithm minimizing pedestrian’s outdoor sunlight exposure based on the spatiotemporal distribution of sunlight in street canyons, which was generated by the simulation of sunlight reaching the ground using Google Street View (GSV) panoramas. The deep convolutional neural network-based image segmentation algorithm PSPNet was used to segment the GSV panoramas into categories of sky, trees, buildings, road, etc. Based on the GSV image segmentation results, we further estimated the spatiotemporal distribution of sunlight in street canyons by projecting the sun path over time on the segmented GSV panoramas. The simulation results in Shibuya, Tokyo, show that the routing algorithm can help to reduce human sunlight exposure significantly compared with the shortest path. The proposed method is highly scalable and can be easily extended to other cities with GSV data available. This study would provide a pedestrian-level strategy to reduce the negative effects of sunlight exposure on urban residents.
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Li, X., Yoshimura, Y., Tu, W., Ratti, C. (2022). A Pedestrian-Level Strategy to Minimize Outdoor Sunlight Exposure. In: Pardalos, P.M., Rassia, S.T., Tsokas, A. (eds) Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities. Springer Optimization and Its Applications, vol 186. Springer, Cham. https://doi.org/10.1007/978-3-030-84459-2_7
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