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This is a Quantitative Study study.

September 30, 2025Australian Journal of Electrical & Electronics Engineering

Computer vision-based quantitative analysis of urban landscape imagery for environmental monitoring and system optimization

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The analysis reveals significant differences in public preferences based on urban landscape composition and colour.
Using a fuzzy c-means clustering algorithm produces accurate colour quantisation from urban imagery.
A quantitative measurement model connects landscape features with public preferences, enhancing evaluation objectivity.
The method demonstrates effectiveness in measuring urban landscape imagery, offering insights into public orientation.

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Implication

This analysis develops a measurement model linking landscape elements and public preferences in urban environments, indicating a new approach to evaluation.

Cite This Study

Ying Li (2025) studied this question.

synapsesocial.com/papers/68dc1e358a7d58c25ebb1776 https://doi.org/10.1080/1448837x.2025.2553956

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Implication

This analysis develops a measurement model linking landscape elements and public preferences in urban environments, indicating a new approach to evaluation.

Computer vision-based quantitative analysis of urban landscape imagery for environmental monitoring and system optimization

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Discussion

Implication

Cite This Study

Computer vision-based quantitative analysis of urban landscape imagery for environmental monitoring and system optimization

Key Points

Discussion

Implication

Cite This Study

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