scholarly journals Spatiotemporal patterns of street-level solar radiation estimated using Google Street View in a high-density urban environment

2019 ◽  
Vol 148 ◽  
pp. 547-566 ◽  
Author(s):  
Fang-Ying Gong ◽  
Zhao-Cheng Zeng ◽  
Edward Ng ◽  
Leslie K. Norford
Keyword(s):  
Solar Radiation ◽  
Urban Environment ◽  
High Density ◽  
Spatiotemporal Patterns ◽  
Google Street View ◽  
Street Level ◽  
Street View

scholarly journals Estimation of Mean Radiant Temperature in Urban Canyons Using Google Street View: A Case Study on Seoul

2022 ◽  
Vol 14 (2) ◽  
pp. 260
Author(s):  
Eun-Sub Kim ◽  
Seok-Hwan Yun ◽  
Chae-Yeon Park ◽  
Han-Kyul Heo ◽  
Dong-Kun Lee
Keyword(s):  
Heat Stress ◽  
Spatial Patterns ◽  
High Density ◽  
View Factor ◽  
Mean Radiant Temperature ◽  
Street Canyons ◽  
Google Street View ◽  
Street Level ◽  
Street View ◽  
Radiant Temperature

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.

Street-level solar radiation mapping and patterns profiling using Baidu Street View images

2021 ◽  
pp. 103289
Author(s):  
Mingyu Deng ◽  
Wei Yang ◽  
Chao Chen ◽  
Zhou Wu ◽  
Yong Liu ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Street Level ◽  
Street View

Google Street View: Capturing the World at Street Level

Computer ◽  
2010 ◽  
Vol 43 (6) ◽  
pp. 32-38 ◽  
Author(s):  
Dragomir Anguelov ◽  
Carole Dulong ◽  
Daniel Filip ◽  
Christian Frueh ◽  
Stéphane Lafon ◽  
...  
Keyword(s):  
Google Street View ◽  
Street Level ◽  
Street View ◽  
The World

Street-level: Google Street View’s abstraction by datafication

2017 ◽  
Vol 20 (3) ◽  
pp. 1201-1219 ◽  
Author(s):  
Aaron Shapiro
Keyword(s):  
Media Studies ◽  
Final Section ◽  
Algorithm Design ◽  
Google Street View ◽  
Street Level ◽  
Street View ◽  
Big Data Applications ◽  
Critical Media ◽  
Criminological Theories ◽  
Urban Places

While aerial photography is associated with vertical objectivity and spatial abstractions, street-level imagery appears less political in its orientation to the particularities of place. I contest this assumption, showing how the aggregation of street-level imagery into “big datasets” allows for the algorithmic sorting of places by their street-level visual qualities. This occurs through an abstraction by “datafication,” inscribing new power geometries onto urban places through algorithmic linkages between visual environmental qualities, geographic information, and valuations of social worth and risk. Though largely missing from media studies of Google Street View, similar issues have been raised in critiques of criminological theories that use place as a proxy for risk. Comparing the Broken Windows theory of criminogenesis with big data applications of street-level imagery informs a critical media studies approach to Google Street View. The final section of this article suggests alternative theoretical orientations for algorithm design that avoid the pitfalls of essentialist equations of place with social character.

Assessing street-level urban greenery using Google Street View and a modified green view index

2015 ◽  
Vol 14 (3) ◽  
pp. 675-685 ◽  
Author(s):  
Xiaojiang Li ◽  
Chuanrong Zhang ◽  
Weidong Li ◽  
Robert Ricard ◽  
Qingyan Meng ◽  
...  
Keyword(s):  
Google Street View ◽  
Street Level ◽  
Urban Greenery ◽  
Street View
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