Automatic modelling of 3D trees using aerial LIDAR point cloud data and deep learning

R. G. Kippers, L. Moth, S. J. Oude Elberink

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

3D tree objects can be used in various applications, like estimation of physiological equivalent temperature (PET). During this project, a method is designed to extract 3D tree objects from a country-wide point cloud. To apply this method on large scale, the algorithm needs to be efficient. Extraction of trees is done in two steps: point-wise classification using the PointNet deep learning network, and Watershed segmentation to split points into individual trees. After that, 3D tree models are made. The method is evaluated on 3 areas, a park, city center and housing block in the city of Deventer, the Netherlands. This resulted into an average accuracy of 92% and a F1-score of 0.96.
Original languageEnglish
Title of host publicationThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publicationXXIV ISPRS Congress "Imaging today, foreseeing tomorrow", Commission II
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages179-184
Number of pages6
VolumeXLIII-B2-2021
EditionB2-2021
DOIs
Publication statusPublished - 28 Jun 2021

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)1682-1750

Keywords

  • ITC-GOLD

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