# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 3 Results
### Comparison with SVF Estimation from 3D Urban Building Models  
The SVF derived from BSV images is compared with results obtained from 3D urban simulations in the Qinhuai District. Firstly, the height of the camera on the BSV image acquisition vehicle is approximately 2 m. As a result, the height of the SVF 2D sampling points is standardized to 2 m and subsequently converted into 3D points. Secondly, the vector data of 2D buildings in the Qinhuai District, which includes the floor field, is multiplied by 3 to determine the height of each building, effectively transforming it into 3D buildings. The \"skyline\" function is then employed to generate 3D skylines, depicting the visible portion of the sky for the input SVF 3D points. Finally, based on the generated 3D skylines, the \"skyline graph\" tool is utilized in a separate Python script within ArcGIS Pro 3.0 to calculate the SVF value (see Figure 11).  
The results demonstrate that the root mean square error (RMSE) of the SVF estimation based on the 3D urban building models and the BMapSVF method is 0.3445, the mean bias error (MBE) is \(-0.2816\), and the correlation coefficient \({}^{\text{\textregistered}}\) is 0.5972 (see Figure 12). The former exclusively focuses on SVF values within the architectural context, while the latter is influenced by spatial factors in complex street view images, such as trees. In the selected study area, characterized by lower tree coverage, both methods yield an RMSE of 0.0442, MBE of \(-0.0339\), and R of 0.9784. The interval of SVF values with higher correlation is within [0.8, 1.0]. In these street view images, factors affecting the sky domain are minimal, and the geometric shape of the sky region in fisheye images is similar to that in skyline graphs. For regions with more tree coverage, the RMSE is 0.7699, MBE is 0.4642, and R is 0.6894. The correlation of SVF values is lower in these SVI, where factors influencing the sky domain are more prevalent, with trees being the most typical. When trees obstruct the sky domain, although the geometric shape of the sky region in fisheye images has some similarity with that in skyline graphs, the segmentation performance of BMapSVF for dense trees is suboptimal, leading to a decrease in SVF estimation. This paper refers to these influencing factors as spatial uncertainty.  
Figure 10: Statistical analysis of the SVF proportions for the Qinhuai District, the blue dots represent the SVF for each sampling point, while the red dots serve as an example of SVF with significant variations due to factors such as trees and buildings causing obstruction.  
Figure 11: Fisheye images and the corresponding skyline graphs at the sampling points, the red dots represent the sampling points.  
Figure 12: The relationship between the SVF estimated based on BMapSVF and that based on the 3D urban building models: (**a**) comparing the results of two SVF approximation methods that consider  
spatial uncertainty; (**b**) exploring the relationship between the two methods in scenarios where trees are sparsely distributed in BSV images; (**c**) investigating the relationship between the two methods in cases with abundant trees in BSV images; and (**d**) offering examples depicting fisheye images and skyline graphs for both sparse and dense tree scenarios.