Enhancing Tree Segmentation in Large Forest Point Clouds with Synthetic Data

Monitoring and preserving forests is becoming increasingly important due to the escalating effects of climate change and threats of deforestation. In the domain of forest science, three-dimensional data acquired through remote sensing technology has gained prominence for its ability to provide deep insights into the complex nature of forest environments. The process of identifying and segmenting individual trees in three-dimensional point clouds is a crucial yet challenging prerequisite for many forest analyses such as the classification of tree health and species. Tree segmentation is currently dominated by classical approaches that often rely on the forest’s canopy height model to identify tree crowns, but with limited success in complex environments and in particular areas underneath the canopy. Recent deep learning models are adept at performing instance segmentation on point clouds, but the performance of these models relies on the quantity and quality of training data. The difficulty of obtaining forest data owing to the cost of technology and annotation process hinders the development of neural networks for tree segmentation in forest point clouds. In this thesis, a scalable workflow is presented to produce arbitrarily large quantities of synthetic forest point clouds, and its effectiveness in deep learning is demonstrated. It is shown that by applying large amounts of synthetic forest data to pretrain neural networks, the individual tree segmentation performance in synthetic and real forests is significantly improved, outperforming classical segmentation methods. It is concluded that this workflow is effective at producing large quantities of realistic forest data, and its incorporation in deep learning fosters progress in tackling tree segmentation in forest point clouds. Its efficiency and scalability further indicate its potential for the development of frameworks, benchmarking systems, high throughput data analysis, and other analytical tasks.

随着气候变化影响加剧以及森林砍伐威胁升级，森林监测与保护的重要性日益凸显。在森林科学领域，通过遥感技术获取的三维数据因能够深入解析森林环境的复杂属性而备受重视。在三维点云（three-dimensional point clouds）中识别并分割单株树木，是诸多森林分析任务（如树木健康与物种分类）的关键且极具挑战性的前提条件。当前树木分割任务主要依赖经典方法，这类方法通常借助森林冠层高度模型识别树冠，但在复杂环境及冠层下方区域的分割效果欠佳。近期的深度学习模型虽擅长在点云上完成实例分割任务，但这类模型的性能高度依赖训练数据的数量与质量。由于技术成本与标注流程的限制，森林数据的获取难度较大，这阻碍了面向森林点云树木分割任务的神经网络研发。本论文提出了一种可扩展工作流，可生成任意规模的合成森林点云，并验证了其在深度学习场景中的有效性。研究表明，通过利用大量合成森林数据对神经网络进行预训练，可显著提升合成与真实森林场景下的单株树木分割性能，其效果优于经典分割方法。综上，该工作流可高效生成大量逼真的森林数据，将其融入深度学习流程，有助于推动森林点云树木分割任务的研究进展。该工作流的高效性与可扩展性，进一步展现了其在框架开发、基准测试系统搭建、高通量数据分析及其他分析任务中的应用潜力。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于利用合成数据增强大规模森林点云中的树木分割，旨在解决传统方法在复杂环境（如树冠下区域）中分割效果有限的问题。通过提出可扩展的合成森林点云生成工作流程，并应用于深度学习预训练，显著提升了合成和真实森林中的个体树木分割性能，超越了经典分割方法。数据集包含一篇硕士论文PDF文件，详细阐述了该方法在促进森林点云分析、框架开发和基准测试方面的潜力。