Vegetation is crucial for sustainable and resilient cities providing various ecosystem services and well-being of humans. However, vegetation is under critical stress with rapid urbanization and expanding infrastructure footprints. Consequently, mapping of this vegetation is essential in the urban environment. Recently, deep learning for point cloud semantic segmentation has shown significant progress. Advanced models attempt to obtain state-of-the-art performance on benchmark datasets, comprising multiple classes and representing real world scenarios. However, class specific segmentation with respect to vegetation points has not been explored. Therefore, selection of a deep learning model for vegetation points segmentation is ambiguous. To address this problem, we provide a comprehensive assessment of point-based deep learning models for semantic segmentation of vegetation class. We have selected four representative point-based models, namely PointCNN, KPConv (omni-supervised), RandLANet and SCFNet. These models are investigated on three different datasets, specifically Chandigarh, Toronto3D and Kerala, which are characterized by diverse nature of vegetation, varying scene complexity and changing per-point features. PointCNN achieves the highest mIoU on the Chandigarh (93.32%) and Kerala datasets (85.68%) while KPConv (omni-supervised) provides the highest mIoU on the Toronto3D dataset (91.26%). The paper develops a deeper insight, hitherto not reported, into the working of these models for vegetation segmentation and outlines the ingredients that should be included in a model specifically for vegetation segmentation. This paper is a step towards the development of a novel architecture for vegetation points segmentation.

翻译：植被对可持续和韧性城市至关重要，它提供多种生态系统服务并保障人类福祉。然而，随着快速城市化和基础设施足迹的扩张，植被正面临严重压力。因此，在城市环境中测绘植被至关重要。近年来，基于深度学习的点云语义分割已取得显著进展。先进模型试图在包含多类且代表真实场景的基准数据集上获得最先进性能。然而，针对植被点的特定类别分割尚未得到充分探索。因此，植被点云分割深度学习模型的选择尚不明确。为解决该问题，我们针对植被类别的语义分割，对基于点的深度学习模型进行了全面评估。我们选取了四种代表性基于点的模型：PointCNN、KPConv（全监督）、RandLANet和SCFNet。这些模型在三个不同数据集（昌迪加尔、Toronto3D和喀拉拉邦）上进行了研究，这些数据集以植被多样性、场景复杂度变化及逐点特征差异为特征。PointCNN在昌迪加尔（93.32%）和喀拉拉邦（85.68%）数据集上取得了最高mIoU，而KPConv（全监督）在Toronto3D数据集上取得了最高mIoU（91.26%）。本文对这些模型在植被分割中的工作机制进行了前所未有的深入剖析，并归纳了专用于植被分割模型应包含的关键要素。本研究为开发新型植被点云分割架构迈出了重要一步。