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Method of semantic segmentation of airborne laser scanning data of water protection zones 

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This article presents an evaluation of the efficiency of a neural network method for the semantic segmentation of three-dimensional point clouds obtained using the Geoscan 401 Lidar UAV. The proposed implementation of the neural network is based on the PointNet++ deep learning model which directly processes point clouds. A technique has been developed for acquiring and preparing a dataset with four classes: land, vegetation, vehicles, and construction objects. To increase the accuracy of the evaluation, a technique based on augmentation and redistribution of the datasets has been proposed. The neural network model consists of hierarchically constructed blocks that perform sampling, grouping, and feature extraction. Adjusting the number of blocks and setting the search radius for local features affects both the accuracy of segmentation and computational costs. The efficiency of the method for semantic segmentation of three-dimensional point clouds obtained using the Geoscan 401 Lidar UAV has been evaluated. The augmentation and redistribution technique improved the average Intersection over Union (IoU) value by at least 35 %. For the obtained data, the optimal radius in the grouping layer was determined, ensuring a balance between detail and sensitivity. It was found that an increase in the number of points in the dataset does not lead to a significant improvement in accuracy; however, the diversity of the datasets used enhances the method efficiency. The developed dataset increases the effectiveness of the applied approach, including when training other models. The results of this study indicate the potential for using the proposed methods and algorithms in constructing digital models of the Amur River and its main tributaries.

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