3D LiDAR Transformer for City-scale Vegetation Segmentation and Biomass Estimation

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationThe International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney, Australia
Publication statusAccepted/In press - 2022
EventInternational Conference on Digital Image Computing: Techniques and Applications 2022: DICTA 2022 - Rydges World Square, Sydney, Australia
Duration: 30 Nov 202202 Dec 2022
http://dicta2022.dictaconference.org/ (Conference website)
http://dicta2022.dictaconference.org/full_program.html#oral4 (Program)

Conference

ConferenceInternational Conference on Digital Image Computing: Techniques and Applications 2022
Country/TerritoryAustralia
CitySydney
Period30/11/2202/12/22
Internet address

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