  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.
