# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 1 Introduction  
Urban spatial morphology refers to the spatial structure and form of the physical environment and various activities in a city [1; 2]. One of the crucial indicators employed in the examination of urban spatial form is the sky view factor (SVF). The SVF is defined as the ratio of the visible sky area to the total visible area within the field of view in an urban space, a range from 0 to 1, where 0 denotes areas completely obstructed, and 1 signifies open spaces. This metric delineates the proportion between the visible sky and the areas obstructed by buildings or trees [3]. Urban design and planning benefit significantly from the SVF as it serves as a direct reflection of the quality of urban spatial characteristics and physical and living environments. Furthermore, it finds widespread application in the assessment of the outdoor thermal environment [4; 5; 6; 7], urban heat island effects [8; 9; 10; 11], public activity comfort [1; 12], and urban planning [13; 14]. Several methods have been developed for SVF measurement, primarily encompassing the geometric method [8; 15; 16], fisheye photographic method [17; 18; 19], GPS-based method [20; 21], and three-dimensional simulation-based method [12; 22; 23; 24; 25]. These methodologies necessitate extensive field data collection and exhibit low efficiency, rendering their application challenging in expansive urban areas. Instant panoramic images of vital locations along the main streets of a city, showcasing pedestrian and vehicular traffic, billboards, buildings, the sky, and trees, are captured by internet street view maps (see Figure 1). 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 ofmethods for extracting the SVF based on SVI, namely classical methods and deep learning methods, are currently receiving increased attention. Geometric or analytical techniques are employed by classical methods to model lighting and derive the SVF value at a specific point. The calculation of the SVF and solar radiation involved using Google Street View (GSV) in conjunction with open-source software Hugin-2023.0.0 and Rayman manual version 0.1. This process reconstructed the fisheye image and relevant irradiance within an urban canyon [27]. The accuracy of this approach was determined to be satisfactory when compared to images captured using a fisheye camera. GSV provided 90\({}^{\circ}\) field-of-view images, subsequently transformed into a hemisphere view through equal-angle projection. Identification of sky and non-sky pixels was accomplished using an improved Sobel filter and Flood Fill algorithm. The SVF distribution for 15 cities was then computed, contributing to the enrichment of the global urban canopy parameters (UCPs) database [1]. AI technology and deep learning algorithms are harnessed by deep learning methods to train SVI and derive characteristic parameters for estimating SVF values. In their study, [PERSON] et al. [28] utilized GSV and SegNet [29] to classify SVI and proposed an automated method for estimating SVF from a substantial volume of SVI. This method was validated for SVF estimation by comparing it with SVF estimates based on digital surface models (DSMs) obtained from light detection and ranging (LiDAR) and three-dimensional city models derived from oblique aerial photogrammetry. [PERSON] et al. [24] presented a workflow for estimating SVF using numerous SVI obtained from sampling points in the urban road network, with the sampling points set at an approximate height of 2 m. They also developed a tool for batch sky area detection and SVF calculation, confirming the performance of sky area segmentation and SVF calculation through photos taken using a fisheye lens. [PERSON] et al.