Abstract
Extreme heat exposure has severe negative impacts on humans, and the issue is exacerbated by climate change. Estimating spatial heat stress such as mean radiant temperature (MRT) is currently difficult to apply at city scale. This study constructed a method for estimating the MRT of street canyons using Google Street View (GSV) images and investigated its large-scale spatial patterns at street level. We used image segmentation using deep learning to calculate the view factor (VF) and project panorama into fisheye images. We calculated sun paths to estimate MRT using panorama images from Google Street View. This paper shows that regression analysis can be used to validate between estimated short-wave, long-wave radiation and the measurement data at seven field measurements in the clear-sky (0.97 and 0.77, respectively). Additionally, we compared the calculated MRT and land surface temperature (LST) from Landsat 8 on a city scale. As a result of investigating spatial patterns of MRT in Seoul, South Korea, we found that a high MRT of street canyons (>59.4 °C) is mainly distributed in open space areas and compact low-rise density buildings where the sky view factor is 0.6–1.0 and the building view factor (BVF) is 0.35–0.5, or west-east oriented street canyons with an SVF of 0.3–0.55. However, high-density buildings (BVF: 0.4–0.6) or high-density tree areas (Tree View Factor, TVF: 0.6–0.99) showed low MRT (<47.6). The mapped MRT results had a similar spatial distribution to the LST; however, the MRT was lower than the LST in low tree density or low-rise high-density building areas. The method proposed in this study is suitable for a complex urban environment consisting of buildings, trees, and streets. This will help decision makers understand spatial patterns of heat stress at the street level.
Highlights
Introduction published maps and institutional affilExposure to heat may cause severe illnesses and deaths during intense heat events, especially in large urban areas, because of altered urban climate conditions [1,2,3]
This study focused on: (1) how to improve the calculation of the mean radiant temperature (MRT) with shortwave and longwave radiation at the street level using publicly available panorama images; (2) investigating the effects of street canyon geometries and morphologies on street-level shortwave radiation; and (3) analyzing the relationship between land surface temperature (LST) and MRT
Our developed method was verified using field measurements in various regions and we demonstrated that the clear-sky solar radiance of street canyons accurately captures the diurnal cycle in high-density environments (R2 = 0.97)
Summary
Exposure to heat may cause severe illnesses and deaths during intense heat events, especially in large urban areas, because of altered urban climate conditions [1,2,3]. As future climate change scenarios of heat-related diseases and mortality have become a major public health issue, a higher spatiotemporal assessment map of heat stress in cities is needed [4,5,6,7,8]. Several studies have used satellite images to retrieve the land surface temperature (LST) in large cities, which evaluated the urban thermal environment and determined the relationship between microclimate conditions and human health-related heat stress or urban geometry [9,10,11,12]. LST-derived from satellite imagery provides results for establishing local measurements of heat exposure. Satellites are insufficient for estimating heat stress in iations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
