
Visual landscape quality represents a potential attribute of landscapes that affects people’s perception and psychological well-being. With the perceived sensory dimensions as a conceptual framework, this study proposes methods to measure visual quality in both real environments and virtual models, using image-based metrics from computer vision techniques and 3D model-based metrics from parametric modelling techniques. Using the Clementi Woods Park in Singapore as a case study, we compared these two types of metrics using statistical methods and proposed an approach of using a regression model from empirical studies to estimate subjective preference for design scenarios and thus to evaluate the result of landscape design scenarios.
Autor / Author: | Xudong, Zhang; Lin, Shengwei; Qi, Jinda; Tan, Puay Yok |
Institution / Institution: | National University of Singapore/Singapore; National University of Singapore/Singapore; National University of Singapore/Singapore; National University of Singapore/Singapore |
Seitenzahl / Pages: | 11 |
Sprache / Language: | Englisch |
Veröffentlichung / Publication: | JoDLA – Journal of Digital Landscape Architecture, 8-2023 |
Tagung / Conference: | Digital Landscape Architecture 2023 – Future Resilient Landscapes |
Veranstaltungsort, -datum / Venue, Date: | Dessau Campus of Anhalt University, Germany 24-05-23 - 27-05-23 |
Schlüsselwörter (de): | |
Keywords (en): | Image-based metrics, 3D model-based metrics, visual landscape quality |
Paper review type: | Full Paper Review |
DOI: | doi:10.14627/537740018 |
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