Quantitative Measurement and Preference Research of Urban Landscape Environmental Image Based on Computer Vision

At present, research on landscape preferences mostly uses traditional questionnaire surveys to obtain public aesthetic attitudes, and the analysis method still relies on manual coding with small sample sizes. However, the research on landscape preference of applying network big data and computer vision technology is rare, and the research content and algorithm application are limited. In order to improve the research effect of quantitative measurement and preference of urban landscape environment image, the algorithm proposed in this paper combines two-dimensional analysis modules, two-dimensional visual domain analysis and three-dimensional visual analysis, and makes full use of the advantages of the two analysis modules, and analyzes the scale from large scale to medium and micro scale based on different accuracy urban digital models. Through image classification and content recognition, image semantic segmentation and image color quantification, the landscape feature information in pictures is mined, and the dimension of landscape image is put forward based on this. In addition, this paper combines experimental analysis to verify that the method proposed in this paper has certain results. It is not only suitable for visual analysis of landmark buildings and landmark structures in cities, but also can analyze the visual characteristics of natural landscapes as urban images in cities. Therefore, the quantitative method of urban visual landscape analysis proposed in this paper can provide reliable data support for the follow-up urban design work.

@article{Wang2024,
  title     = {Quantitative Measurement and Preference Research of Urban Landscape Environmental Image Based on Computer Vision},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yan Wang},
  doi       = {10.14569/IJACSA.2024.0150810},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150810}
}