scholarly journals Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach

2017 ◽  
Vol 9 (5) ◽  
pp. 411 ◽  
Author(s):  
Jianming Liang ◽  
Jianhua Gong ◽  
Jun Sun ◽  
Jieping Zhou ◽  
Wenhang Li ◽  
...  
Keyword(s):  
Big Data ◽  
View Factor ◽  
Sky View Factor ◽  
Street View ◽  
Factor Estimation

scholarly journals Proposal of System for Calculating Sky View Factor Using Google Street View

2021 ◽  
pp. 313-324
Author(s):  
Shoko Nishio ◽  
Fumiko Ito
Keyword(s):  
Big Data ◽  
View Factor ◽  
Panoramic Image ◽  
Original Image ◽  
Google Street View ◽  
Sky View Factor ◽  
Street View ◽  
Current Point ◽  
Extended Area ◽  
The Web

AbstractIn recent years, big data entered use in various fields. Google Street View (hereinafter called “GSV”) can be regarded as open big data, and its images can be obtained using API. The streets can be viewed 360° horizontally and 290° vertically from each point on the web. In addition to those, zooming up is available, and the viewpoint can be moved approximately 10 m forward or backward to/from the current point. The original image to provide the view is the panoramic image associated with the latitude and longitude information on the street consecutively at intervals of 10 m, and they exist as massive data on the web. We determine the area of the sky using these images from GSV. In this research, we calculate the sky view factor (hereinafter called “SVF”) in an extended area by defining the area of the sky with the SVF and utilizing the computer.

scholarly journals Application of Method for Calculating Sky View Factor Using Google Street View: Relation Between Sky View Factor and Physical Elements in Urban Space

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Shoko Nishio ◽  
Fumiko Ito
Keyword(s):  
Urban Space ◽  
Computation Method ◽  
View Factor ◽  
Statistical Process ◽  
Google Street View ◽  
Sky View Factor ◽  
Street View ◽  
The Difference ◽  
Physical Elements

<p><strong>Abstract.</strong> We applied a computation method of calculating the sky view factor (SVF) using Google Street View to Shibuya area, Tokyo, for the purpose of examining the relation between the SVF/SVF change and physical elements. The distribution of the SVF calculated by the above method was visualized, and the statistical process showed the tendency of a high SVF in quasi-residential districts and roadsides of high-graded trunk roads. The difference in the SVF change was small at 10-m intervals. The SVF change tended to be more apparent near an intersection and at different elevations.</p>

GSV2SVF-an interactive GIS tool for sky, tree and building view factor estimation from street view photographs

2020 ◽  
Vol 168 ◽  
pp. 106475 ◽  
Author(s):  
Jianming Liang ◽  
Jianhua Gong ◽  
Jinming Zhang ◽  
Yi Li ◽  
Dong Wu ◽  
...  
Keyword(s):  
View Factor ◽  
Street View ◽  
Factor Estimation

scholarly journals Sky view factor and thermal comfort analysis using hemispheric images from Google Street View and wavelet in an urban ecosystem of the Brazilian Cerrado

2018 ◽  
Vol 40 ◽  
pp. 26
Author(s):  
Angela Fatima da Rocha ◽  
Ernany Paranaguá da Silva ◽  
Carlo Ralph de Musis ◽  
Marta Cristina de Albuquerque Nogueira
Keyword(s):  
Thermal Comfort ◽  
Air Temperature ◽  
View Factor ◽  
Brazilian Cerrado ◽  
Physiological Equivalent Temperature ◽  
Mato Grosso ◽  
Google Street View ◽  
Sky View Factor ◽  
Street View ◽  
Temperature And Humidity

This article aims to analyse the sky view factor (SVF) in one of the hottest cities of the Brazilian Cerrado, and its correlation with thermal comfort in two urban sections with different characteristics, as well as the physiological equivalent temperature (PET) and predicted mean vote (PMV) indices, complemented by a characterisation in the frequency field for a 12-month cut-off in the same year of relative air temperature and humidity. The study area was located in the central region of Cuiabá, Mato Grosso, due to the presence of regions with high urbanisation indices and small parks; one section composed of afforested area and second section composed of varied buildings. To obtain the SVF, the Google Street View image database was used, from which fisheye images were reconstructed and the SVF was determined using  RayMan  software. The PET and PMV indices were determined for the morning, afternoon, and evening, with comfort in the morning and discomfort in the afternoon and evening. Traditional Morlet wavelets were plotted for time series of relative air temperature and humidity for the year 2015, which qualitatively demonstrated some of the dynamics of these micrometeorological variables for tropical Cerrado climate.

Positional error modeling of sky‐view factor measurements within urban street canyons

2021 ◽  
Author(s):  
Zian Wang ◽  
Guoan Tang ◽  
Guonian Lü ◽  
Cheng Ye ◽  
Fangzhuo Zhou ◽  
...  
Keyword(s):  
Error Modeling ◽  
View Factor ◽  
Positional Error ◽  
Street Canyons ◽  
Urban Street ◽  
Sky View Factor ◽  
Urban Street Canyons

scholarly journals Calculation of the irradiance of solar radiation in a greenhouse with a complex structure using a diagram for sky view factor

2020 ◽  
Vol 76 (1) ◽  
pp. 44-52
Author(s):  
Shuh MATSUDA ◽  
Hisashi YOSHIKOSHI ◽  
Tomoyo SUZUKI ◽  
Yuuki OHTA ◽  
Ayaka CHIBA ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Complex Structure ◽  
View Factor ◽  
Sky View Factor

Development of synthetic hemispheric projections suitable for assessing the sky view factor on vertical planes

2015 ◽  
Vol 74 ◽  
pp. 279-286 ◽  
Author(s):  
J. Ramírez-Faz ◽  
R. López-Luque ◽  
F.J. Casares
Keyword(s):  
View Factor ◽  
Sky View Factor

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.

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