Assessment of forest condition provides important information about the health status and biodiversity of the forest. At the same time, it is a long-lasting process that needs to be carried out regularly. In this thesis, we investigated methods for segmenting individual trees from LiDAR data, which could simplify and accelerate the process. We tested two already developed segmentation methods. The first was based on generating a Canopy Height Model (CHM) and region growing. The second used a deep learning model and was based on the U-Net architecture. Both methods were tested on our dataset. The methods achieved an accuracy of 77\ \%. We showed that the deep learning method is more successful for the segmentation of smaller trees and dense coniferous forests. We substantiated the potential of deep learning for the considered task.
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