Using Google Street View for Street-Level Urban Form Analysis, a Case Study in Cambridge, Massachusetts

2019 ◽  
pp. 457-470 ◽  
Author(s):  
Xiaojiang Li ◽  
Carlo Ratti
Keyword(s):  
Urban Form ◽  
Form Analysis ◽  
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.

scholarly journals Assessing Species Habitat Using Google Street View: A Case Study of Cliff-Nesting Vultures

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e54582 ◽  
Author(s):  
Pedro P. Olea ◽  
Patricia Mateo-Tomás
Keyword(s):  
Google Street View ◽  
Street View

scholarly journals Assessing soil sealing and ecosystem services of urban front yards using Google Street View: A case study in Bloemhof district Rotterdam, the Netherlands

2022 ◽  
Vol 955 (1) ◽  
pp. 012019
Author(s):  
TH Heikoop
Keyword(s):  
Climate Change ◽  
Green Space ◽  
Environmental Benefit ◽  
Google Street View ◽  
Soil Sealing ◽  
Considerable Portion ◽  
Street View ◽  
Very High ◽  
Impacts Of Climate Change

Abstract Private gardens play an important role as urban green space in cities and can improve the microclimate and address the impacts of climate change. Paving over front yards, soil sealing, reduces the environmental benefit of front yards. Residential private front yards comprise a considerable portion of land and green space in the suburbs of cities. Currently there is no method available to determine sealed soil percentages of private front yards. This study took place in the Bloemhof suburban district in Rotterdam. Four streets were selected and a total of 123 houses with 105 private front yards were assessed. Five sealed soil reference categories were defined and Google Street View (GSV) images were used to assess the front yards. This study found that the aggregated sealed soil percentages of the private front yards in the four selected streets are very high: 69%, 78%, 96%, and 97%. These front yards have a significant greening potential. The new insight in this study is that the use of Google Street View images for categorisation of front yards leads to values for individual front yard that can be used for comparison and for establishing sealed soil values per street.

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
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