Research on the Perception Evaluation of Urban Green Spaces Using Panoramic Images and Deep Learning: A Case Study of Zhujiang Park in Guangzhou
Xukai ZHAO, Guangsi LIN
Abstract
Visual quality assessment of urban green spaces is a major topic in landscape architecture research, yet traditional methods face limitations in practice. The rapid development of artificial intelligence and street-view big data offers opportunities for advancing green space perception studies. However, the lack of full street view image coverage of green spaces in China poses challenges for related research. Focusing on public landscape perception evaluation, this research took Zhujiang Park in Guangzhou, China as a case study. The research team utilized a convenient image collection method by panoramic camera and an effective processing workflow, and then employed the Segformer-B5 semantic segmentation model and the ViT-base-p16 image classification model to calculate four objective evaluation metrics (green view index, sky view factor, road visibility index, and artificial structure visibility index) and four subjective evaluation metrics (attractiveness, richness, naturalness, and depression) for visual quality assessment. Based on the spatial distribution results of these metrics, comprehensive analyses were conducted and low-score areas were identified. Research results indicate that vegetation and water features significantly enhance park attractiveness and positive perceptions, while excessive sky and artificial structures produce negative effects; oppressive artificial landscapes and constrained architectural views also lower overall landscape quality. The image collection and visual perception evaluation methods proposed in this study provide a scientific basis for the renovation and management of urban green spaces.