ForestTaxator: A tool for detection and approximation of cross-sectional area of trees in a cloud of 3D points

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Maciej Małaszek
Andrzej Zembrzuski
Krzysztof Gajowniczek

Keywords : Point Cloud, Genetic Algorithms, Trees, 3D Scan

In this paper we propose a novel software, named ForestTaxator, supporting terrestrial laser scanning data processing, which for dendrometric tree analysis can be divided into two main processes: tree detection in the point cloud and development of three-dimensional models of individual trees. The usage of genetic algorithms to solve the problem of tree detection in 3D point cloud and its cross-sectional area approximation with ellipse-based model is also presented. The detection and approximation algorithms are proposed and tested using various variants of genetic algorithms. The work proves that the genetic algorithms work very well: the obtained results are consistent with the reference data to a large extent, and the time of genetic calculations is very short. The attractiveness of the presented software is due to the fact that it provides all necessary functionalities used in the forest inventory field. The software is written in C# and runs on the .NET Core platform, which ensures its full portability between Windows, MacOS and Linux. It provides a number of interfaces thus ensuring a high level of modularity. The software and its code are made freely available.

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Małaszek, M., Zembrzuski, A., & Gajowniczek, K. (2022). ForestTaxator: A tool for detection and approximation of cross-sectional area of trees in a cloud of 3D points. Machine Graphics and Vision, 31(1/4), 19–48.

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