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Research on automated optimization of low-carbon architectural landscape spaces based on computer vision and machine learning

Author

Listed:
  • Rongbing Mu
  • Yue Cheng
  • Haoxuan Feng

Abstract

In this study, computer vision and machine learning techniques are used to develop an automatic optimization method for low-carbon building landscape space. Firstly, the semantic segmentation of landscape images is carried out using U-Net network to realize the automatic extraction of key landscape features. Then, using the segmentation results, a multi-objective optimization algorithm is developed. The effectiveness of the proposed method is verified by simulation experiments, which not only significantly improves the efficiency and accuracy of landscape space optimization, but also provides valuable optimization suggestions for designers.

Suggested Citation

  • Rongbing Mu & Yue Cheng & Haoxuan Feng, 2025. "Research on automated optimization of low-carbon architectural landscape spaces based on computer vision and machine learning," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 146-153.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:146-153.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae280
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