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 seven representative point-based models, namely PointCNN, KPConv (omni-supervised), RandLANet, SCFNet, PointNeXt, SPoTr and PointMetaBase. These models are investigated on three different datasets, specifically Chandigarh, Toronto3D and Kerala, which are characterized by diverse nature of vegetation and varying scene complexity combined with changing per-point features and class-wise composition. PointMetaBase and KPConv (omni-supervised) achieve the highest mIoU on the Chandigarh (95.24%) and Toronto3D datasets (91.26%), respectively while PointCNN provides the highest mIoU on the Kerala dataset (85.68%). 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，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扩张，植被面临严峻压力。因此，在城市环境中对植被进行制图至关重要。近年来，用于点云语义分割的深度学习取得了显著进展。先进模型试图在包含多类别且代表真实场景的基准数据集上实现最优性能。然而，针对植被点的类别特异性分割尚未得到充分探索。因此，选择用于植被点分割的深度学习模型存在歧义。为应对这一问题，我们全面评估了基于点的深度学习模型在植被类别语义分割中的表现。我们选取了七个代表性基于点模型：PointCNN、KPConv（全监督）、RandLANet、SCFNet、PointNeXt、SPoTr 及 PointMetaBase。这些模型在三个不同数据集上进行了研究，即 Chandigarh、Toronto3D 和 Kerala，这些数据集以植被多样性、场景复杂度变化以及逐点特征和类别组成的差异为特征。PointMetaBase 和 KPConv（全监督）分别在 Chandigarh（95.24%）和 Toronto3D 数据集（91.26%）上取得最高 mIoU，而 PointCNN 在 Kerala 数据集（85.68%）上获得最高 mIoU。本文深入揭示了这些模型在植被分割中的工作机制（此前未见报道），并概述了专门用于植被分割的模型应包含的要素。本研究为开发新型植被点分割架构迈出了重要一步。