Deep learning for semantic segmentation of airborne laser scanning point clouds

Yaping Lin

Research output: ThesisPhD Thesis - Research UT, graduation UT

366 Downloads (Pure)

Abstract

ALS data are essential data sources used to generate digital terrain models (DTM), 3D city models, landscape models and high precision maps. Semantic segmentation aiming to assign every point with a semantic label of ALS point clouds is of importance when generating those 3D products that have multiple categories and ask for detailed object geometry. Motivated by the top performance of deep learning algorithms on scene understanding tasks, this Ph.D. thesis investigates the semantic segmentation of ALS point clouds based on deep learning algorithms. We first explore how to learn representative features from ALS point clouds (Chapter 2). Then we focus on how to reduce the manual labelling efforts to train a deep learning model for semantic segmentation. We investigate active learning (Chapter 3) to select and annotate informative points, and weak supervision (Chapter 4) to annotate only weak labels for the pointwise prediction task.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Faculty of Geo-Information Science and Earth Observation
  • University of Twente
Supervisors/Advisors
  • Vosselman, George, Supervisor
  • Yang, Michael Ying, Co-Supervisor
Award date7 Sept 2022
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-5410-7
DOIs
Publication statusPublished - 2022

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