A Pedestrian-Level Strategy to Minimize Outdoor Sunlight Exposure

Xiaojiang Li (), Yuji Yoshimura, Wei Tu and Carlo Ratti
Additional contact information
Xiaojiang Li: Temple University
Yuji Yoshimura: Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku
Wei Tu: Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services and Research Institute of Smart Cities, School of Architecture and Urban Planning, Shenzhen University
Carlo Ratti: Massachusetts Institute of Technology

A chapter in Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, 2022, pp 123-134 from Springer

Abstract: 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.

Keywords: Sunlight exposure; Shade routing; Google street view (GSV) (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84459-2_7

Ordering information: This item can be ordered from
http://www.springer.com/9783030844592

DOI: 10.1007/978-3-030-84459-2_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().