# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 1 Introduction
[30] introduced a method based on GSV and SegNet for precisely estimating the street canyon SVF, tree view factor (TVF), and building view factor (BVF) in the high-density urban environment of Hong Kong for street feature extraction. The accuracy of this method was validated using fisheye photos captured in densely populated high-rise and low-rise areas. [PERSON] et al. [31] applied the DeepLabv3+ model [32] for the semantic segmentation and classification of GSV images to automatically extract the sky region, providing a more realistic depiction of the urban street situation. [PERSON] et al. [33] utilized Baidu Street View (BSV) and the DeepLabv3+ model to detect the sky region and proposed an automated SVF calculation method. This method was successfully applied in the central urban area of Shanghai, China, demonstrating the feasibility of using SVI for large-scale SVF calculations in complex and high-density urban environments. [PERSON] et al. [34] developed the user-friendly GIS integrated client tool GSV2 SVF based on [30], simplifying the workflow for automatically extracting the SVF, TVF, and BVF from GSV. Figure 1: Example of the complex spatial information and multiscale phenomenon in SVI. The support of existing software and algorithms are relied upon by classical methods, characterized by high accuracy but demanding the on-site collection of a significant number of fisheye photos, posing challenges for application over a large urban area. This limitation is overcome by a deep learning-based approach. However, the color and target features of SVI are constrained by variations in geography, environment, culture, and climate, making the universal application of network model parameter estimation difficult. Consequently, the primary focus of this study is on enhancing the applicability of deep learning models and automating the system's processing capabilities. Previous methods of fisheye image collection involved either fieldwork or offline retrieval from Google Maps, both accompanied by intricate data processing. The accessibility and affordability of online maps offer a more practical alternative. Given the popularity of BSV in China, and inspired by a prior work [34], this study opted for BSV [35] as the primary data source. By integrating the DeepLabv3 deep learning network into existing open-source web and database platforms, the web platform BMapSVF was constructed for BSV image acquisition, storage, real-time calculation, and visualization. This effectively streamlines the process of refining SVF extraction and analysis from SVI based on domestic online map platforms. It is a well-established practice that commercial online platforms, as a preventive measure against abuse, typically set limitations on access frequency and the download count for specific datasets. User tracking and data access are managed through API keys or alternative authentication methods. This paper adopts an API Key Pool approach, applying for 20 Baidu Maps development keys at once. The BMapSVF system efficiently ensures sustained data downloading and processing through the rotation of keys.