3D LiDAR transformer for city-scale vegetation segmentation and biomass estimation

3D LiDAR has transformed various urban infrastructure management practices, including urban vegetation detection and monitoring. The accessibility and convenience of use of LiDAR observations in ecological investigations has substantially improved because of advancements in LiDAR hardware systems and data processing techniques. In this paper, we introduce a slot attention-based network for semantic segmentation and biomass estimation of vegetation. We named it the 3D semantic vegetation transformer (3DSVT). Our proposed method first extracts point features by exploiting RandLA-Net, which are then passed to slot attention to extract object central features for semantic segmentation. Finally, vegetation biomass is computed based on the resultant semantic segmentation. We compare our proposed approach to the state-of-the-art 3D point cloud semantic segmentation methods on SensatUrban and semantic3D datasets. The experiments show that our proposed method is giving promising results and can thus be used to analyse and compute the vegetation biomass of 3D point clouds at a large scale.