Research on simulation system of landscape art design based on computer learning and virtual reality technology

The increasing growth of urbanization has created significant prospects for the advancement of architectural landscape design. However, the existing machine learning and image processing methods provide partial solutions, because they struggle with noise, overlapping landscape features, and poor segmentation accuracy. To address these limitations, we propose a hybrid simulation and classification model that integrates the advantages of computer learning with immersive virtual reality (VR) environments. First, wavelet-based denoising and intensity normalization are applied to enhance 360° landscape image quality. A multi-orientation segmentation method is then used to accurately classify the complex visual features. Texture features are extracted using a combination of Grey Level Co-occurrence Matrix (GLCM) and Bayesian-optimized Gauss Markov Random Field (GMRF), which helps to capture both spatial and statistical relationships. These features are classified using a hybrid approach combining logistic regression (LR) and K-nearest neighbor (KNN), which allows strong observation of landscape features. Simulation of the model is conducted using real world immersive VR studies. The experiments demonstrate the superiority of the model in terms of accuracy (98%), precision (99.5%), and sensitivity (98.5%), respectively.