Integrating geographic knowledge into self-supervised contrastive learning of street view imagery for representing urban space

In this study, a novel self-supervised contrastive learning framework that integrates geographic knowledge into the analysis of street view imagery for representing urban space is proposed. Traditional methods that rely solely on deep learning often struggle to capture the complex spatial characteristics of urban environments. To address this issue, in the proposed framework, we first extracted visual knowledge (VK) from street view imagery using semantic segmentation and then constructed contrastive samples through VK–imagery pairs. Finally, we introduced distance-weighted temperatures into the contrastive loss function to adjust the similarities of urban features encoded in the representations on the basis of geographical proximity, thereby integrating semantic knowledge among geographical locations (GSK). The method was validated through two case studies: urban village classification in Guangzhou and Foshan and urban-mobility pattern prediction in Shenzhen. The results showed significant improvements in classification accuracy (OA: 0.967) and reduced prediction error (MAE: 28.069) compared with conventional approaches. This research demonstrates the effectiveness of integrating geographic knowledge (including VK and GSK) into contrastive learning. The approach enhances model interpretability and generalizability for urban studies and provides a tool for analyzing urban development patterns and mobility needs that will be useful for urban planning and policymaking.