Resumen
Understanding public perceptions of images of urban public spaces can guide efforts to improve urban vitality and spatial diversity. The rise of social media data and breakthroughs in deep learning frameworks for computer vision provide new opportunities for studying public perceptions in public spaces. While social media research methods already exist for extracting geo-information on public preferences and emotion analysis findings from geodata, this paper aims at deep learning analysis by building a VGG-16 image classification method that enhanced the research content of images without geo-information. In this study, 1940 Flickr images of the Haihe River in Tianjin were identified in multiple scenes with deep learning. The regularized VGG-16 architecture showed high accuracies of 81.75% for the TOP-1 and 96.75% for the TOP-5 and Grad-CAM visualization modules for the interpretation of classification results. The result of the present work indicate that images of the Haihe River are dominated by skyscrapers, bridges, promenades, and urban canals. After using kernel density to visualize the spatial distribution of Flickr images with geodata, it was found that there are three vitality areas in Haihe River. However, the kernel density result also shows that judging spatial visualization based solely on geodata is incomplete. The spatial distribution can be used as an assistant function in the case of the under-representation of geodata. Collectively, the field of how to apply computer vision to urban design research was explored and extended in this trial study.