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Sensitivity analysis of full-waveform LiDAR data for material classification
Citation Link: https://doi.org/10.15480/882.13519
Publikationstyp
Conference Paper
Date Issued
2024-09-18
Sprache
English
Author(s)
Aldohami, Omar
TORE-DOI
Start Page
2
End Page
9
Citation
35. Forum Bauinformatik, fbi 2024: 2-9
Contribution to Conference
Publisher
Technische Universität Hamburg, Institut für Digitales und Autonomes Bauen
Peer Reviewed
true
Full-waveform (FWF) LiDAR can provide additional features which can be helpful to distinguish between different construction materials. However, due to its high volume FWF information is often discarded and not used for downstream analysis, such as material classification. This contribution investigates waveform processing, such as Gaussian decomposition, and modelling to extract important radiometric features from terrestrial laser scanning. In addition, geometric information is extracted by analysing point cloud neighbourhoods. The resulting combination of features is evaluated by means of sensitivity analysis to obtain their corresponding relevance to material classification. Specifically, the assessment is performed leveraging machine learning algorithms, such as support vector machines, and monitoring the influence of the model’s performance depending on the combinatoric inclusion of the proposed individual features. To support the analysis, furthermore a rich dataset is presented, consisting of point clouds, waveform and image data of various urban and infrastructure scenes. Thereby the aim of the classification problem is to semantically segment the point clouds according to common materials such as concrete surfaces, vegetation or brick. This effort serves the goal of improving the automatic digitization of construction assets through the use of advanced remote sensing techniques.
Subjects
Digital Twin
Full-waveform
Laser scanning
Machine learning
Scan-to-BIM
DDC Class
621.3: Electrical Engineering, Electronic Engineering
006: Special computer methods
620: Engineering
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Sensitivity Analysis of Full-waveform LiDAR Data for Material Classification.pdf
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