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

• Published in: Building and environment 2018-04, Vol.134, p.155-167
• Main Authors: Gong, Fang-Ying, Zeng, Zhao-Cheng, Zhang, Fan, Li, Xiaojiang, Ng, Edward, Norford, Leslie K.
• Format: Article
• Language: English
• Subjects: Algorithms; Buildings; Canyons; Deep learning; Density; Energy consumption; Energy transfer; Feature extraction; Google Street View; High density; Machine learning; Photography; Roads & highways; Street canyon; Street canyons; Street trees; Three dimensional models; Trees; Urban areas; Urban environments; View factor
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2018.02.042

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 R2 > 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 (R2 = 0.40, p