Abstract
The segmentation of individual trees from forest point clouds is a crucial task for downstream analyses such as carbon sequestration estimation. Recently, deep-learning-based methods have been proposed which show the potential of learning to segment trees. Since these methods are trained in a supervised way, the question arises how general models can be obtained that are applicable across a wide range of settings. So far, training has been mainly conducted with data from one specific laser scanning type and for specific types of forests. In this work, we train one segmentation model under various conditions, using seven diverse datasets found in literature, to gain insights into the generalization capabilities under domain-shift. Our results suggest that a generalization from coniferous dominated sparse point clouds to deciduous dominated high-resolution point clouds is possible. Conversely, qualitative evidence suggests that generalization from high-resolution to low-resolution point clouds is challenging. This emphasizes the need for forest point clouds with diverse data characteristics for model development. To enrich the available data basis, labeled trees from two previous works were propagated to the complete forest point cloud and are made publicly available at this https URL.
Abstract (translated)
从森林点云中提取单个树木是一个关键的任务，对于诸如碳储存估计等下游分析具有至关重要的意义。最近，基于深度学习的方法已经被提出，展示了从学习分割树木的潜力。由于这些方法以有监督的方式进行训练，因此问题是如何获得适用于各种设置的通用的模型。到目前为止，主要使用来自特定激光扫描类型的数据和特定类型的森林进行训练。在本文中，我们使用来自文献中七种不同数据集的一个分割模型，在各种条件下进行训练，以探究领域漂移下的泛化能力。我们的结果表明，从针叶林点云到落叶林点云的泛化是可能的。相反，定性证据表明，从高分辨率到低分辨率点云的泛化具有挑战性。这强调了需要具有多样数据特征的森林点云来支持模型开发。为了丰富现有的数据基础，来自两个以前工作的带有标签的树木被传播到完整的森林点云，并在此处公开发布，链接在此：https://www.xxx。
URL
https://arxiv.org/abs/2405.02061
