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With the rapid development of virtual reality technology in the field of spatial cognition and environmental simulation, landscape garden planning is in urgent need of integrating high-precision modelling and intelligent path optimization to improve design efficiency and user experience. Aiming at the problems of complex landscape path structure and diverse interaction demands, a virtual landscape planning system based on multi-scale terrain data and multi-objective genetic optimisation algorithm is constructed. The experimental results show that DEM-GA (Multi-objective Elevation-Genetic Fusion Algorithm) outperforms Dijkstra, A* and ACO algorithms in terms of path bifurcation degree (1.68), landscape exposure balance degree (0.88) and interaction fun density (0.137/m). The average computation time is controlled at 1.36 s, the success rate of path generation reaches 98%, the convergence process is smooth, and the comprehensive score of the final scoring session is 4.36, which shows good practicality and user recognition. The system can provide efficient, stable and interactive friendly technical paths for virtual garden planning, and has the potential to be widely used in design practice and teaching scenarios.

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Research on Planning and Design System of Landscape Architecture Area Based on Virtual Reality Technology

  • 会议论文

  • 发表日期：

    2026年

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  • Yi He,
  • Yuqiong Lin,
  • Shixin Wu,
  • Miaoru Chen

摘要

With the rapid development of virtual reality technology in the field of spatial cognition and environmental simulation, landscape garden planning is in urgent need of integrating high-precision modelling and intelligent path optimization to improve design efficiency and user experience. Aiming at the problems of complex landscape path structure and diverse interaction demands, a virtual landscape planning system based on multi-scale terrain data and multi-objective genetic optimisation algorithm is constructed. The experimental results show that DEM-GA (Multi-objective Elevation-Genetic Fusion Algorithm) outperforms Dijkstra, A* and ACO algorithms in terms of path bifurcation degree (1.68), landscape exposure balance degree (0.88) and interaction fun density (0.137/m). The average computation time is controlled at 1.36 s, the success rate of path generation reaches 98%, the convergence process is smooth, and the comprehensive score of the final scoring session is 4.36, which shows good practicality and user recognition. The system can provide efficient, stable and interactive friendly technical paths for virtual garden planning, and has the potential to be widely used in design practice and teaching scenarios.