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Mapping sky, tree, and building view factors of street canyons in a high-density urban environment

Gong, Fang-Ying and Zeng, Zhao-Cheng and Zhang, Fan and Li, Xiaojiang and Ng, Edward and Norford, Leslie K. (2018) Mapping sky, tree, and building view factors of street canyons in a high-density urban environment. Building and Environment, 134 . pp. 155-167. ISSN 0360-1323. https://resolver.caltech.edu/CaltechAUTHORS:20180516-155309399

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Abstract

View factors for sky, trees, and buildings are three important parameters of the urban outdoor environment that describe the geometrical relationship between different surfaces from the perspective of radiative energy transfer. This study develops an approach for accurately estimating sky view factor (SVF), tree view factor (TVF), and building view factor (BVF) of street canyons in the high-density urban environment of Hong Kong using publicly available Google Street View (GSV) images and a deep-learning algorithm for extraction of street features (sky, trees, and buildings). As a result, SVF, TVF, and BVF maps of street canyons are generated. Verification using reference data of hemispheric photography from field surveys in compact high-rise and low-rise areas shows that the GSV-based VF estimates have a satisfying agreement with the reference data (all with R^2 > 0.95), suggesting the effectiveness and high accuracy of the developed method. This is the first reported use of hemispheric photography for direct verification in a GSV-based streetscape study. Furthermore, a comparison between GSV-based and 3D-GIS-based SVFs shows that the two SVF estimates are significantly correlated (R^2 = 0.40, p < 0.01) and show better agreement in high-density areas. However, the latter overestimates SVF by 0.11 on average, and the differences between them are significantly correlated with street trees (R^2 = 0.53): the more street trees, the larger the difference. This suggests that a lack of street trees in a 3D-GIS model of street environments is the dominant factor contributing to the large discrepancies between the two datasets.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.buildenv.2018.02.042DOIArticle
ORCID:
AuthorORCID
Gong, Fang-Ying0000-0001-5194-6435
Zeng, Zhao-Cheng0000-0002-0008-6508
Zhang, Fan0000-0002-3643-018X
Norford, Leslie K.0000-0002-5631-7256
Additional Information:© 2018 Elsevier Ltd. Received 8 December 2017, Revised 2 February 2018, Accepted 26 February 2018, Available online 6 March 2018.
Subject Keywords:View factor; Google Street View; Deep learning; Street trees; Street canyon; High density
Record Number:CaltechAUTHORS:20180516-155309399
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180516-155309399
Official Citation:Fang-Ying Gong, Zhao-Cheng Zeng, Fan Zhang, Xiaojiang Li, Edward Ng, Leslie K. Norford, Mapping sky, tree, and building view factors of street canyons in a high-density urban environment, Building and Environment, Volume 134, 2018, Pages 155-167, ISSN 0360-1323, https://doi.org/10.1016/j.buildenv.2018.02.042. (http://www.sciencedirect.com/science/article/pii/S0360132318301148)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:86432
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 May 2018 17:15
Last Modified:09 Mar 2020 13:19

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