The urban landscape environment is a dynamic system, and its imagery is strongly influenced by public perception, making quantitative measurement challenging. Traditional methods rely on real-scene virtual technology to assess preferences, but lack clear evaluation indicators, leading to subjectivity. This study proposes using computer vision colour quantisation to objectively measure urban landscape imagery and clarify public preference orientations. The fuzzy c-means (FCM) clustering algorithm is applied to pixel points in urban landscape images, producing colour quantisation and constructing a chromaticity matrix to extract local colour features. These features are then input into an extended fully convolutional network to identify landscape elements. Based on this, a quantitative measurement model linking landscape elements and imagery is developed, enabling the analysis of public preferences. Colour, as an intuitive and objective visual element, provides a strong basis for quantification. Case analyses demonstrate the method's effectiveness in measuring urban landscape imagery, revealing differences in public preferences across landscape composition, proportion, and colour. This approach reduces subjectivity and offers a robust framework for quantitative evaluation of urban landscape environments.
Ying Li (Fri,) studied this question.