Using Eye-tracking and Deep Learning Approach to Promote Naturalness in Urban Landscape

Promoting greenness and naturalness has been the integral goal in nature-based solutions for urban environments. Design and building appreciated landscape for subjective public perception is a key factor in the success of promoting urban greenness and naturalness. The current measures of naturalness are siloed from public appreciation and acceptance of urban landscape designs. Our goal is to use state-of-art methods combining traditional design perception evaluation to embed naturalness with public landscape aesthetic perceptions evaluation system. A deep learning and eye-tracking based approach to understand public aesthetic perceptions of landscape street-view images is developed and applied to a case study of Shanghai. We use machine deep learning techniques to identify and assess landscape composition with landscape images and in-situ captured data to study the influence of naturalness of public perceptions of landscape based on a Bayesian network aesthetic evaluation model. The methodology extend the present landscape aesthetic evaluation framework and has the potential to be implemented to much wider applications. Our results indicate a co-conception of naturalness and public appreciation as a proof-of-concept of nature-based solutions.

Key words：Eye-tracking；Deep Learning；Naturalness；Public aesthetic perceptions；Bayesian network aesthetic evaluation