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LiDAR (Light Detection and Ranging) technology is now commonly used in geospatial technology when it is an active remote sensing technology and capable of collecting data on large areas. However, with a large dataset of measurement areas, selecting and using the data in accordance with the research purpose takes a lot of time to conduct the classification of points. The algorithm selection in LiDAR data processing problem is important in the use of lidar data. EM (Expectation Maximization) algorithm is a typical algorithm of data mining, with the advantage of being easy to install and understand the algorithm used in classification problems. In this study, the author improved the EM algorithm in classification of lidar point cloud by using scheduling parameters to reduce the convergence time of the algorithm.

Currently, the power grid and grid safety corridor are still issues of concern to the electricity industry. Every year, there are still many tragic accidents when violating the electricity grid safety corridor. In addition, unidentified objects and foreign bodies are also one of the hazards that can affect power lines and leave serious consequences. The problem is that it is necessary to separate the safety corridor of the power grid, power lines and the surrounding area so that warnings can be made quickly and promptly. To solve this problem, it is necessary to have a set of data that is collected quickly, processing data quickly and accurately. This paper deals with the use of LiDAR point cloud data in power line segregation and grid safety corridors. With the experimental results performed, the accuracy of the problem of separating the safety corridor of the power grid, power lines and adjacent areas with an accuracy of over 90% is a reliable proof for the suitability of the grid. LiDAR dataset, data processing technology with research problem.

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