Deep Convolutional Neural Network Based Single Tree Detection Using Volumetric Module From Airborne Lidar Data

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dc.contributor.advisorSohn, Gunho
dc.contributor.advisorKo, Connie
dc.contributor.authorLee, Hyungju
dc.date.accessioned2023-03-28T21:24:11Z
dc.date.available2023-03-28T21:24:11Z
dc.date.copyright2022-12-09
dc.date.issued2023-03-28
dc.date.updated2023-03-28T21:24:11Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractThere was an undeniable success of Deep Learning networks for visual data analytics such as object detection and segmentation in recent years, while the adaptation to tree detection has been rare. In this paper, we pursue to achieve individual tree identification, defined as a detection of an individual tree as each object, with deep convolutional neural networks to create and update tree inventories using LiDAR information. The first objective was to provide a suitable dataset that can be used to test such networks and to create a module that attempts to increase the 3D object detection algorithms' detection accuracy. This novel dataset was created by fusing LiDAR data gathered by Teledyne Optech with field data collected by York University. The second was to develop an appropriate accuracy increasing volumetric module. For this module, the learnable weights concept was introduced, which enable to increase detection precision of the object detection algorithm.
dc.identifier.urihttp://hdl.handle.net/10315/41046
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.subjectComputer science
dc.subjectUrban forestry
dc.subject.keywordsMachine learning
dc.subject.keywordsDeep learning
dc.subject.keywordsObject detection
dc.subject.keywords3D object detection
dc.subject.keywordsComputer vision
dc.subject.keywordsLiDAR
dc.subject.keywordsPoint cloud
dc.subject.keywordsLoss function
dc.subject.keywordsVolumetric module
dc.subject.keywordsTree detection
dc.titleDeep Convolutional Neural Network Based Single Tree Detection Using Volumetric Module From Airborne Lidar Data
dc.typeElectronic Thesis or Dissertation

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