Landscape layout characteristics and evaluation of smart buildings based on deep learning algorithms

With the acceleration of global urbanization and the improvement of people’s expectations for quality of life, intelligent buildings have become an essential part of future urban development, especially in terms of landscape layout and design. How to achieve aesthetics, practicality, and sustainability Unity has become a critical issue that needs to be solved urgently. Based on the landscape layout of intelligent buildings, this study proposes an evaluation framework based on a deep learning algorithm, aiming at improving the quality and efficiency of landscape design through intelligent means. Traditional landscape design often depends on the empirical judgment of designers, needs more objective quantitative evaluation standards, and is challenging to adapt to large-scale and diversified application scenarios. Therefore, this study applies deep convolutional neural networks to the characteristic analysis and optimization of intelligent building landscape layout. By studying a large number of excellent landscape design cases, the model can independently extract the key elements that constitute an excellent landscape layout and generate high-quality personalized design solutions accordingly. Through experimental verification, the optimized model has significantly improved design novelty, environmental adaptability, and user satisfaction, reaching an increase of 15%, 20%, and 12%, respectively. This study also constructs a comprehensive evaluation system, covering visual aesthetics, ecological benefits, interactive experience, and other dimensions to quantify the effect of landscape layout. With the help of the data processing capabilities of deep learning, various design schemes can be comprehensively and objectively evaluated, providing strong support for the selection and decision-making of innovative building projects.