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

Rongbing Mu, Yue Cheng and Haoxuan Feng

International Journal of Low-Carbon Technologies, 2025, vol. 20, 146-153

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.

Keywords: low-carbon building; landscape space optimization; computer vision; machine learning; multi-objective optimization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:146-153.

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