Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data

Article

Tarsha Kurdi, Fayez, Amakhchan, Wijdan and Gharineiat, Zahra. 2021. "Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data." International Journal of Environmental Sciences and Natural Resources. 28 (2). https://doi.org/10.19080/IJESNR.2021.28.556234
Article Title

Random forest machine learning technique for automatic vegetation detection and modelling in LiDAR data

Article CategoryArticle
AuthorsTarsha Kurdi, Fayez (Author), Amakhchan, Wijdan (Author) and Gharineiat, Zahra (Author)
Journal TitleInternational Journal of Environmental Sciences and Natural Resources
Journal Citation28 (2)
Article Number556234
Number of Pages4
Year2021
PublisherJuniper Publishers
Place of PublicationIrvine, California, United States
ISSN2572-1119
Digital Object Identifier (DOI)https://doi.org/10.19080/IJESNR.2021.28.556234
Web Address (URL)https://juniperpublishers.com/ijesnr/IJESNR.MS.ID.556234.php
Abstract

Machine learning techniques have gained a distinguished position in the automatic processing of Light Detection and Ranging (LiDAR) data area. They represent the actual research topic in the remote sensing domain. Indeed, this paper presents one method of supervised machine learning, which is called Random Forest. This algorithm is discussed, and their primary applications in automatic vegetation extraction and modelling in the LiDAR data area are presented here.

KeywordsLiDAR; random forest; classification; modelling
ANZSRC Field of Research 2020339999. Other built environment and design not elsewhere classified
Byline AffiliationsGriffith University
Abdelmalek Essaâdi University, Morocco
School of Civil Engineering and Surveying
Institution of OriginUniversity of Southern Queensland
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