Automated Determination of Forest-Vegetation Characteristics with the Use of a Neural Network of Deep Learning

摘要

The article proposes a method of automated solution for determining the species composition, stock coefficient and other characteristics of forest plantations with the use of deep learning. The analysis of existing approaches and ways of forest inventory, which include the use of LiDAR systems and machine learning methods, is carried out. An algorithm is proposed for solving this problem and features of its implementation are given. The problem of combining the data of a "dense cloud" and a lidar survey is considered, a possible solution is proposed. The problem of segmentation of tree crowns among many other objects in this data is also considered. For the segmentation of crowns, it is proposed to use the PointNet neural network of deep learning, which allows segmentation of objects by submitting a point cloud to the input. The description of the architecture and the main features of the neural network use are briefly given. The path of further research is determined.