The natural focus: Combining deep learning and eye-tracking to understand public perceptions of urban ecosystem aesthetics

Abstract

Public perceptions of ecological aesthetics are a crucial component in evaluations of the quality of urban green spaces (UGSs) and in guiding urban planning, design and renewal practices. In this paper, we propose a novel methodological framework that combines deep learning and eye-tracking to investigate the relationships between landscape elements, features, visual focus and public ecosystem aesthetic perceptions of UGSs. Our Public Ecosystem Aesthetic Perceptions (PEAP) model emphasises interactions between ecosystem quality and landscape aesthetic quality. To validate the model, we conducted a study in Shanghai, China, where we selected 54 representative UGSs for photography, perception surveys, and image processing. The results demonstrated that visual focus acts as a mediator between landscape stimuli (landscape elements and features) and perceptions of ecosystem aesthetics. Notably, we found that natural elements elicited significantly stronger visual focus than artificial elements. Thus, our PEAP approach can enhance understanding of aesthetic qualities of landscapes and ecological qualities of urban ecosystems, enabling creation of more aesthetically pleasing and ecologically sustainable UGSs.

Keywords

• Eye-tracking
• Deep Learning
• Public Ecosystem Aesthetic Perceptions (PEAP)
• Urban Green Space
• Aesthetic Ecosystem Services