Application of High-Spatial-Resolution Imagery and Deep Learning Algorithms to Spatial Allocation of Urban Parks’ Supply and Demand in Beijing, China

Abstract

The development of green spaces in urban parks can significantly enhance the quality of the urban and ecological environment. This paper utilizes 2021 Gaofen-7 (GF-7) satellite remote sensing images as its primary data source and uses deep learning algorithms for the precise extraction of the green space coverage within Beijing’s fifth ring road. It also incorporates the park points of interest (POI) information, road data, and other auxiliary data to extract green park space details. The analysis focuses on examining the relationship between supply and demand in the spatial allocation of green park spaces from an accessibility perspective. The main findings are as follows: (1) The application of deep learning algorithms improves the accuracy of green space extraction by 10.68% compared to conventional machine methods. (2) The distribution of parks and green spaces within the fifth ring road of Beijing is uneven, showing a clear pattern of “more in the north and less in the south”. The accessibility within a five-minute service radius achieves a coverage rate of 46.65%, with a discernible blind zone in the southeast. (3) There is an imbalance in the per capita green space location entropy within the fifth ring road of Beijing, there is a big difference in per capita green space location entropy (44.19), and social fairness needs to be improved. The study’s outcomes unveil the intricate relationship between service capacity and spatial allocation, shedding light on the supply and demand dynamics of parks and green spaces within Beijing’s fifth ring road. This insight will contribute to the construction of ecologically sustainable and aesthetically pleasing living spaces in modern megacities.