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FuelDeep3D  

3D Fuel Segmentation Using Terrestrial Laser Scanning and Deep Learning
View on CRAN: Click here


Download and install FuelDeep3D package within the R console
Install from CRAN:
install.packages("FuelDeep3D")

Install from Github:
library("remotes")
install_github("cran/FuelDeep3D")

Install by package version:
library("remotes")
install_version("FuelDeep3D", "0.1.1")



Attach the package and use:
library("FuelDeep3D")
Maintained by
Venkata Siva Reddy Naga
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-02
Latest Update: 2026-03-02
Description:
Provides tools for preprocessing, feature extraction, and segmentation of three-dimensional forest point clouds derived from terrestrial laser scanning. Functions support creating height-above-ground (HAG) metrics, tiling, and sampling point clouds, generating training datasets, applying trained models to new point clouds, and producing per-point fuel classes such as stems, branches, foliage, and surface fuels. These tools support workflows for forest structure analysis, wildfire behavior modeling, and fuel complexity assessment. Deep learning segmentation relies on the PointNeXt architecture described by Qian et al. (2022) <doi:10.48550/arXiv.2206.04670>, while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang et al. (2016) <doi:10.3390/rs8060501>.
How to cite:
Venkata Siva Reddy Naga (2026). FuelDeep3D: 3D Fuel Segmentation Using Terrestrial Laser Scanning and Deep Learning. R package version 0.1.1, https://cran.r-project.org/web/packages/FuelDeep3D. Accessed 25 Oct. 2026.
Previous versions and publish date:
No previous versions
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View FuelDeep3D citation profile
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Complete documentation for FuelDeep3D
Functions, R codes and Examples using the FuelDeep3D R package
Full FuelDeep3D package functions and examples
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