Application of Lightweight Deep Learning Model in Landscape Architecture Planning and Design

The holistic view of garden construction is firstly reflected in the integration of the elements that make up the garden, and the primary and secondary are distinguished from the perspective of the whole city, the continuation of the upper planning, the coordination with the surrounding groups and the harmony of the internal gardening elements. The primary goal of ANN (artificial neural network) learning is to understand the drawings and to convert information such as plant numbers and positions in digital drawings into standard digital formats for storage. In front of the SSD (Single Shot Multi-box Dettor) network model, a standard architecture network for image classification is adopted, called the basic network and is fused for comprehensive detection. This paper proposes the network model flow of the 3D object voxel modeling method based on the lightweight DL (Deep learning) model. The cyclic 2D encoder, cyclic 3D decoder and view planner are integrated into a unified framework responsible for feature extraction and fusion, feature decoding and view planning. The results show that the pixel accuracy, the average accuracy and the average IU value are the highest, with the pixel accuracy as high as 90.44%, the average accuracy as high as 93.15%, and the average IU value as 92.72%. In landscape image processing, it provides a certain foundation for future landscape planning and design.

@article{Zhang2023,
  title     = {Application of Lightweight Deep Learning Model in Landscape Architecture Planning and Design},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Linyu Zhang},
  doi       = {10.14569/IJACSA.2023.0141081},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141081}
}