Cross-platform complementarity: Assessing the data quality and availability of Google Street View and Baidu Street View

Google Street View (GSV) and Baidu Street View (BSV) cover most of the world’s cities. However, their combined use and research application still lack a unified standard. We selected Hong Kong Special Administrative Regions of China (SAR), a region where both types of street view image (SVI) are available, as the study area. In this study, we developed an SVI collection method based on a search-oriented approach. Using an SQLite database to manage over 700,000 SVIs, we conducted comparative experiments. Deep learning methods were employed to process the data and extract visual elements from both types of SVI. We performed a comprehensive comparison of the data quality and usability of the two SVIs in terms of temporal coverage, data acquisition efficiency, acquisition repeatability, and visual element similarity. The findings indicate that BSV outperforms GSV in terms of acquisition efficiency and repeatability, while GSV shows better temporal coverage. The results also confirmed high correlations between GSV and BSV in visual elements related to buildings, greenery, and sky, with correlation coefficients (R) of 0.781, 0.664, and 0.653, respectively. The comparative framework proposed in this study provides theoretical support for geoinformation researchers utilizing SVI data for global-scale studies.