TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds

• URL: http://arxiv.org/abs/2309.08471v3
• Date: Mon, 06 Jan 2025 15:26:26 GMT
• Title: TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds
• Authors: Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander Ecker,
• Abstract summary: TreeLearn is a deep learning approach for tree instance segmentation of forest point clouds. TreeLearn is trained on already segmented point clouds in a data-driven manner. We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software.
• Score: 40.46280139210502
• License:
• Abstract: Laser-scanned point clouds of forests make it possible to extract valuable information for forest management. To consider single trees, a forest point cloud needs to be segmented into individual tree point clouds. Existing segmentation methods are usually based on hand-crafted algorithms, such as identifying trunks and growing trees from them, and face difficulties in dense forests with overlapping tree crowns. In this study, we propose TreeLearn, a deep learning-based approach for tree instance segmentation of forest point clouds. TreeLearn is trained on already segmented point clouds in a data-driven manner, making it less reliant on predefined features and algorithms. Furthermore, TreeLearn is implemented as a fully automatic pipeline and does not rely on extensive hyperparameter tuning, which makes it easy to use. Additionally, we introduce a new manually segmented benchmark forest dataset containing 156 full trees. The data is generated by mobile laser scanning and contributes to create a larger and more diverse data basis for model development and fine-grained instance segmentation evaluation. We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software. An evaluation on the benchmark dataset shows that TreeLearn performs as well as the algorithm used to generate its training data. Furthermore, the performance can be vastly improved by fine-tuning the model using manually annotated datasets. We evaluate TreeLearn on our benchmark dataset and the Wytham Woods dataset, outperforming the recent SegmentAnyTree, ForAINet and TLS2Trees methods. The TreeLearn code and all datasets that were created in the course of this work are made publicly available.

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