Tarsha Kurdi, Fayez and 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):556234.
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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.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | This work is licensed under Creative Commons Attribution 4.0 License. |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -) |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -) |
| Date Deposited: | 19 Jul 2021 05:38 |
| Last Modified: | 19 Jul 2021 05:38 |
| Uncontrolled Keywords: | LiDAR; random forest; classification; modelling |
| Fields of Research (2008): | 09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing 09 Engineering > 0909 Geomatic Engineering > 090999 Geomatic Engineering not elsewhere classified 01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified |
| Fields of Research (2020): | 33 BUILT ENVIRONMENT AND DESIGN > 3399 Other built environment and design > 339999 Other built environment and design not elsewhere classified |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences |
| Socio-Economic Objectives (2020): | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280104 Expanding knowledge in built environment and design |
| Identification Number or DOI: | doi:10.19080/IJESNR.2021.28.556234 |
| URI: | http://eprints.usq.edu.au/id/eprint/42809 |
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