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

Abstract

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.

Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing

“…A total of 208,746 street view images were obtained using the Tencent static picture API. For more detail on the crawling technique, see [34,46]. The massive street view image dataset that we developed was adopted as a surrogate of urban streetscapes in Beijing, and was used to perform further analysis over a large geographical area and to develop new metrics for the hedonic pricing model in the study.…”

Knowledge Atlas on the Relationship between Urban Street Space and Residents’ Health—A Bibliometric Analysis Based on VOSviewer and CiteSpace

“…However, from the co-occurrence clustering and the emergence of this field, it is noticeable that Environmental Science and Ecology, Geography, Urban, Education and Educational Research, Multidisciplinary, Arts and Humanities are prevalent, revealing cross disciplinary research tendency in this area. Psychological restoration and physical activity to explain the health promoting value of green space [42,43]; Regional climate change [44]; Living away from green spaces may increase the risk of obesity [45]; High-albedo materials [46]; The sky view factor (SVF), significantly affects outdoor thermal environments [47,48]; Google Street View (GSV), urban greenery assessment tool [49,50];Three mechanisms through which greenery might exert its positive effect on health: stress reduction, stimulating physical activity and facilitating social cohesion [51][52][53].…”

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

“…Many recently developed landscape assessment methods use semantic segmentation [34,[44][45][46], also called scene labeling, which refers to the process of assigning a semantic label (e.g., vegetation, buildings, and cars) to each pixel of an image. In the QLA360 methodology, the same results in the form of classified images are obtained.…”