Cloud modeling method of large-scale ALS building points of repetitive buildings based on automatic sensitivity

Abstract

The invention relates to a cloud modeling method of large-scale ALS building points of repetitive buildings based on automatic sensitivity. The method comprises following steps: (1) adopting a deep learning method, precisely cutting ALS point clouds and obtaining four kinds of targets such as buildings, plants, grounds and others; (2) detecting repetitive buildings in local areas as for cloud points of repetitive buildings and adopting a data driving method for rectification and alignment of detected repetitive buildings to construct a roof model for repetitive buildings; taking a mixed model building method including comprehensive data driving and model driving for reminding non-repetitive buildings and constructing a geometric model for building roofs; (3) making qualitative and quantitative evaluations as for precision and efficiency of the modeling model for geometric models of building roofs. The method has following advantages: 1) modeling efficiency and precision is high so that city neighborhoods of many repetitive buildings can be suitably modeled; 2) the method facilitates integration with other methods in order to increase application scope of modeling methods and layered details of models.

Description

technical field [0001] The invention relates to a large-scale ALS building point cloud modeling method under repeated building automatic perception, and belongs to the field of remote sensing science and technology. Background technique [0002] With the development and application of spatial information technology, people's demand for data is getting higher and higher. Traditional two-dimensional data can no longer fully meet the needs of daily life and production. With 3S technology, high-performance computing and three-dimensional visualization With the development of technology, it is completely possible to efficiently acquire and process massive two-dimensional or three-dimensional data, such as Google Earth developed by Google and Bing Maps service launched by Microsoft, which allows people to roam in a virtual world composed of three-dimensional models , to experience an immersive artistic conception, the rich spatial information of 3D data helps to express the object...

AI Technical Summary

Problems solved by technology

[0011] (1) Although the simplified modeling method using digital surface models is more suitable for dealing with complex free-form buildings and generating LOD (Level of Detail, LOD) models of different scales, which is convenient for network transmission and visual rendering, the ALS data Integrity and noise are more sensitive, and it is difficult to guarantee the regularity of the geometric appearance of the model. In addition, there is no unified standard for how to simplify the model. In addition, this method has high time complexity and is not suitable for large-scale urban buildings. modeling;

[0012] (2) Reverse semantic process modeling is convenient for editing the structure of the building model (the number of floors, the number of windows in each floor, etc.), but there is no unified model for the compilation of semantic rules, and corresponding semantic rules need to be defined for different building structures, and The optimization link has high time complexity and is not suitable for reconstruction of large-area buildings;

[0013] (3) The data-driven modeling method does not need to assume the roof structure type of the building in advance. In theory, any roof type can be modeled, but the segmentation of roof structure elements often has a high time complexity, and even requires the help of human-computer interaction In addition, this type of method is also sensitive to the noise, density, uniformity and integrity of the ALS data, and the quality of the data seriously affects the accuracy of the final model;

[0014] (4) The model-driven modeling method is not sensitive to the quality of ALS data, and the constructed model has the characteristics of compactness, lightness (consisting of fewer triangles) and seamlessness, but it is only suitable for parameterization To express simple buildings, even with the help of "CSG" modeling ideas to deal with complex structural buildings, the primitive library of the limited parameter model library is difficult to completely match the changing buildings in the real world

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