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Open AccessArticle

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

by 1,2,3,*, 1,3,*, 1,3, 1,3, 1,3, 1,3, 1,4,5 and 1,4
1
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
School of Life Sciences, Arizona State University, P.O. Box 874501, Tempe, AZ 85287, USA
3
Zhejiang-CAS Application Center for Geoinformatics, Jiashan 314100, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
5
National Marine Data and Information Service, Tianjin 300171, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Lei Wang, Qiusheng Wu, Josef Kellndorfer and Prasad S. Thenkabail
Remote Sens. 2017, 9(5), 411; https://doi.org/10.3390/rs9050411
Received: 8 April 2017 / Revised: 8 April 2017 / Accepted: 22 April 2017 / Published: 30 April 2017
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications. View Full-Text
Keywords: sky view factor; Google Street View; panorama; automatic extraction; urban climate; urban planning sky view factor; Google Street View; panorama; automatic extraction; urban climate; urban planning
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MDPI and ACS Style

Liang, J.; Gong, J.; Sun, J.; Zhou, J.; Li, W.; Li, Y.; Liu, J.; Shen, S. Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach. Remote Sens. 2017, 9, 411. https://doi.org/10.3390/rs9050411

AMA Style

Liang J, Gong J, Sun J, Zhou J, Li W, Li Y, Liu J, Shen S. Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach. Remote Sensing. 2017; 9(5):411. https://doi.org/10.3390/rs9050411

Chicago/Turabian Style

Liang, Jianming; Gong, Jianhua; Sun, Jun; Zhou, Jieping; Li, Wenhang; Li, Yi; Liu, Jin; Shen, Shen. 2017. "Automatic Sky View Factor Estimation from Street View Photographs—A Big Data Approach" Remote Sens. 9, no. 5: 411. https://doi.org/10.3390/rs9050411

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