A parallel point cloud generation DEM method for predicting calculation intensity based on machine learning

Abstract

The invention provides a parallel point cloud DEM generation method based on machine learning prediction calculation intensity, and the method is characterized in that the method comprises the steps:carrying out the training of a CART model including feature selection, generation of sample data, model training, and storage. Using a quadtree to recursively divide to-be-processed point cloud data,using a stored CART model to predict the computational strength of each tile, and based on the Z-curve, carrying out dimension reduction processing on the tiles of the two-dimensional space, and thenmapping the tiles to each process based on the predicted tile calculation intensity and spatial coding for parallel interpolation of DEM. The technical scheme of the invention has high efficiency andfeasibility. Compared with a traditional serial point cloud generation DEM, the parallel point cloud generation DEM method based on machine learning prediction calculation intensity is adopted, performance improvement can achieve breakthrough of the magnitude, execution time is effectively saved, and the DEM method is particularly suitable for data processing of mass dense point clouds and supports geology application real-time performance.

Description

technical field [0001] The invention belongs to the technical field of network geographic information system application, and is a fast and efficient method for generating DEM (Digital Elevation Model, digital elevation model) based on point cloud, specifically a method capable of realizing point cloud generation DEM in a parallel manner. Compared with the traditional serial processing method, the method can achieve an order of magnitude breakthrough in time, and the idea of ​​the proposed method can be used in other geospatial data processing fields. Background technique [0002] In recent years, with the rapid development of data acquisition technology, the acquisition of dense point clouds has become more and more common. However, how to efficiently and quickly process raw point cloud data and extract useful information from it, such as using point clouds to quickly generate DEM, is still a challenge. . High-performance computing has been widely used in the field of GIS ...

AI Technical Summary

Problems solved by technology

However, point cloud, as a kind of spatial data, usually has spatial heterogeneity in its distribution, and the calculation intensity of the point cloud interpolation DEM algorithm does not only depend on the number of point clouds. The calculation intensity evaluation method will cause serious load imbalance and reduce the efficiency of parallelization