With the development of unmanned aerial vehicle (UAV) LiDAR technology, large-scale high-precision point cloud data is gradually playing an important role in the classification and extraction of terrain and features. This paper constructs a point cloud classification model based on an improved convolutional neural network (CNN) for the surface unmanned aerial vehicle (UAV) LiDAR point cloud data in a certain area of Sichuan Province, with ground points, building points, and vegetation points as categories. Firstly, the categories of the original point cloud are manually labeled to create the training set and test set, and the geometric and intensity features of each point are extracted. Then, a multi-layer CNN model is constructed using PyTorch to conduct end-to-end classification training and model testing on the point cloud. The experimental results show that the overall classification accuracy (OA) can reach 93.6599%, and the classification effect on ground points and vegetation points is especially good.
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