A Deep Learning Approach to Identify Potential Sites for Pocket Park Installation in Nanjing, China

Pocket parks are considered valuable tools for supporting green space provision in high-density environments. As the factors affecting pocket parks vary in fine-grained granularity, analyzing them efficiently on a large scale using traditional methods is challenging. Therefore, considering Nanjing as the study site, a potential identification framework was established, which includes categories of space, vitality, facility, and pleasurability for pocket park installation (PPI) based on visual information from street view images (SVIs). Then, the TrueSkill algorithm was employed to construct four parallel-task training sets according to the four categories and a convolutional neural network (CNN) and active learning (AL) approaches were applied to establish four parallel-task models of deep learning (DL). A two-phase grading score method was designed to integrate the identification results of the four DL models into a comprehensive potential score to determine priority sites for PPI. Lastly, the attributes of the potential sites and corresponding planning strategies in different scenarios were analyzed. The results showed that this modelling approach can integrally assess multiple factors on PPI within an enormous scope, overcoming traditional methods' spatial and dimensional constraint in high-density environments. Our method and results can inform future pocket park planning and design in Nanjing and similar cities worldwide.