# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 4 Discussion  
In a certain sense, with the continuous and extensive coverage of BSV, BMapSVF enables the real-time collection of numerous panoramic images for calculation SVF values  
Figure 14: Uncertainty results of semantic segmentation networks in SVI. Predictive entropy maps represent qualitative results of uncertainty. Darker shades indicate higher uncertainty [46]. Figure 13: Pre- and post-comparison images and SVF values for removing spatial uncertainty in BSV. and generates corresponding spatial databases for the respective areas. The SVF values computed using BMapSVF can contribute to a better understanding of urban morphological characteristics. Moreover, they can be further utilized to investigate the relationships between the SVF and urban heat island effects, solar radiation, spatial environmental quality, and other factors. These findings provide a scientific basis for rational urban planning and development, offering valuable insights for decision-making processes. Although it is not difficult for urban researchers to calculate the SVF using simple scripts, BMapSVF offers more ease of operation and user-friendliness in an online and visualized format that can be used in a browser as soon as it is deployed. Users can see the fisheye image and SVF values for each sampling point on the map. As of now, BMapSVF can be applied to different urban environments in China. The validation in this study was conducted in a research area with relatively dense building structures. To obtain test data for different urban environments, the source code of this tool has been made available in this paper, enabling users to enhance the adaptability of BMapSVF across diverse urban settings. The correct use of this tool requires the user to upload the correct sampling point file or to sample a single point directly on Baidu Map to ensure that the other functions work properly. During the semantic segmentation of BSV panoramas, we found that our prediction model had limited capability in representing vegetation features, especially with seasonal variations. The density and coverage of vegetation significantly affect the estimation of the SVF. Therefore, it might be necessary to improve the ability of the model to accurately segment vegetation, particularly in complex scenes to obtain high-precision SVF values. In Section 3.3, the 3D urban building models-based method was employed to compute the SVF; the results indicated a significant discrepancy between the two methods. In a study conducted by [28], the SVF estimated based on the LiDAR-Derived Digital Surface Model (DSM) was compared with the SVF estimated from SVI, yielding a correlation coefficient of 0.2818. Subsequently, we considered the influence of vegetation factors and obtained a correlation coefficient of 0.5972. The SVF estimation method based on 3D urban building models uses building floor numbers provided by Gaode Map without considering trees factors. The significant difference between the proposed method and the SVF estimation method based on BSV images can also be attributed to this factor. This indirectly reflects that Gaode Map offers a rapid channel for acquiring three-dimensional building data, presenting considerable advantages in utilizing online resources for SVF calculations. In addition, the time of street view image acquisition and the change in building data will also affect the results of SVF estimation. In urban scenes, the SVF value is affected by spatial uncertainty factors, especially the occlusion of trees. In general, quantitatively describing the impact of trees is a challenging issue in SVF studies. The complex geometrical shapes of trees and the seasonal changes in crown volume caused by leaf growth and falling leaves force researchers to ignore trees or apply simple geometric objects [47]. Therefore, SVI will still be employed as a data source, studying to further quantify the effect of trees on the accuracy of the SVF and to remove trees based on the context of the image in the future.