Study on Landscape Architecture Model Design Based on Big Data Intelligence

Abstract

Abstract Because of the rapid development of Internet technology in recent years, the speed of information data growth is faster and faster, through the use of Internet information data to landscape architecture analysis and research, has become the main direction of industry development. Landscape planners are involved in a wide range of projects, and architectural objects are often very complex in layout. Projects are usually composed of multiple components. For the point clouds corresponding to these objects, the direct reconstruction of them is relatively complex, which requires macroscopic and reasonable planning on the regional scale. Generally, plots should be designed in three-dimensional space. The contents presented by planning and design of different scales are also different. In dealing with multi-level regional planning and design, this paper mainly focuses on the parametric design technology research of landscape architecture. First, it starts from the design of large areas, analyzes the status quo of regional scale, and then starts to design with the help of three-dimensional model. First will get the clusters of 3 d model the coarse segmentation class continue to split into more rules part, according to the characteristics of the model data, the first of 3 d model to differential information to estimate the point cloud data, calculate the normal vector and curvature of point cloud data, through a point cloud registration technology will point cloud unified under different Angle of view to the same coordinate system, through the big data landscape algorithm for geometric feature and image feature point detection, detailed point cloud data processing algorithms in the process of coarse segmentation of regional scale in the same cluster of different object segmentation, again through the panoramic image segmentation for interior point cloud of geometric structure information. The data can be classified intelligently, the corresponding point cloud data can be marked, and the geometric structure information can be marked into the point cloud data to be segmented through point cloud matching, so as to enhance knowledge reserves, find problems and solve problems timely.