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

Abstract

The sky‐view factor (SVF) in the urban street canyon is a single point‐specific measurement that can only represent the ratio of the visible sky of a specific point. The positional error of the SVF observation point (PE‐SVFOP) to the measurement of the SVF for specific applications is often ignored. This study conducted a quantitative exploration of the positional error in SVF measurements (PE‐SVF) by comparing the SVF estimated at the desired SVF observation point and the corresponding actual SVF observation point. A numerical geometric‐based method was applied in urban street canyons with five typical aspect ratios to explore the effect of both the lateral and vertical positional error of SVF observation points on the measurement of SVF. An empirical case study using a simulation‐based method was then conducted with a 3D city model of a typical street canyon in Hong Kong. The empirical results show that the average and maximum PE‐SVFs were −0.0051 and 0.0181, respectively, and the average and maximum relative PE‐SVFs (RPE‐SVFs) were 7.68 and 20.37%, respectively. An open‐source toolbox was developed to evaluate the PE‐SVF and RPE‐SVF, which can assist specific SVF applications with a more comprehensive consideration of the PE‐SVF.

Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China

“…This visible horizon is then depicted as the "skyline graph" (see Figure 5b), illustrating the angle of elevation to the skyline against the azimuth direction, providing a comprehensive view of the visible parts of the sky from the observer's location. Equation ( 7) was utilized to ascertain SVF values from a specific SVF observation point using a generated skyline graph [26]:…”

“…The requested parameters for BSV images, including the developer API key, image size, longitude and latitude coordinates, and horizontal field of view, are instrumental in the acquisition process. Through the BSV panorama API, URL requests are initiated to Baidu Maps, initiating a search the BSV panoramas within a specified locale and obtaining metadata in return [26] (see Figure 7). Should multiple BSV panoramas be available, the API automatically furnishes information regarding the BSV panorama in closest proximity to the input search center, with a predetermined radius of 50 m. Within the MySQL database, the storage encompasses panoramas sourced from BSV images, accompanied by their intrinsic attribute information and the resultant SVF calculations, with the table's fundamental structure outlined in Table 3.…”

“…These data offer an intuitive representation of the spatial morphology of specific locations within the city. Street view imagery (SVI) sourced from online map providers, such as Google Maps and Baidu Maps have transformed into contemporary data sources for generating hemispherical images [26]. Two types of effectively streamlines the process of refining SVF extraction and analysis from SVI based on domestic online map platforms.…”

Evaluating building color harmoniousness in a historic district intelligently: An algorithm-driven approach using street-view images

“…Nevertheless, it is difficult to collect data sources using the aforementioned methods, making it difficult to depict the building façade color on a larger scale. Fortunately, SVIs, which record information about the urban environment, have become increasingly available more recently, helping to describe the characterization of the physical environment (Wang et al, 2021) and human activities in urban areas (Zhang et al, 2019).…”

Automatic Detection and Assessment of Pavement Marking Defects with Street View Imagery at the City Scale

“…Massive amounts of street-view imageries are sent to the cloud server and are accessible via the internet. By extensively photographing urban road networks, street-view imageries have played an important role in the efficient monitoring of, and quantitative research on, the urban environment [10][11][12]. However, detecting pavement markings with street-view imageries is challenging, since vehicle-mounted cameras shoot these images from a horizontal angle and under various lighting conditions.…”