Evaluation of the Visual Perception of Urban Single/Double-Layer Riverfront Greenway Landscapes Based on Deep Learning

Abstract

Urban inland rivers are closely related to urban development, but high-density urbanisation has reduced the natural function of streams and the riverbanks are hardened into two parts, embankment walls and berms, which give rise to a variety of riparian landscapes. However, the difference in the height of riparian walkways affects the degree of their greening and landscape effects. In this paper, we studied single- and double-decker urban greenways, constructed quantitative indicators of spatial elements based on deep learning algorithms using an image semantic segmentation (ISS) model that simulates human visual perception, used random forests and multivariate linear regression models to study the impact of the height difference of the linear riverfront greenway on visual perception, clarified the impact of the visual landscape differences caused by different types of space on landscape aesthetic preferences (LP) and confirmed the impact of the specific extent to which landscape components influence preferences. The results of the study showed that there were significant differences in landscape perception scores between the single and double layers. (1) The influence of WED (negative correlation) and NI (positive correlation) is large in the single-layer greenway. The colour, material and structure of the guardrail can be beautified and diversified and the quality of the greenery can be taken into account to maintain the visibility of the greenery in order to improve the score of the single-layer greenway. (2) The significant influence of BVI in the double-layered greenway is positive. Water-friendly or water-viewing spaces can be added appropriately to improve the landscape score of double-layered greenways. This study is applicable to the regional landscape feature identification of single- and double-decker greenways on large-scale urban hard barge bank images, which realises the whole-region feature identification of a large-scale human perspective and is an effective expansion of analysis techniques for sustainable landscape planning and the design of riparian greenways.