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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "FuelDeep3D" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'FuelDeep3D: 3D Fuel Segmentation Using Terrestrial Laser Scanning and Deep
  Learning'
version: 0.1.1
doi: 10.32614/CRAN.package.FuelDeep3D
abstract: 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) <https://doi.org/10.48550/arXiv.2206.04670>,
  while ground classification utilizes the Cloth Simulation Filter algorithm by Zhang
  et al. (2016) <https://doi.org/10.3390/rs8060501>.
authors:
- family-names: Naga
  given-names: Venkata Siva Reddy
  email: venkatasivareddy003@gmail.com
- family-names: Gaskins
  given-names: Alexander John
  email: alexandergaskins@ufl.edu
- family-names: Silva
  given-names: Carlos Alberto
  email: c.silva@ufl.edu
repository: https://venkatasivanaga.r-universe.dev
repository-code: https://github.com/venkatasivanaga/FuelDeep3D
commit: 98f29d11f572b1f1e53d1fde1aca604ed78bba11
url: https://github.com/venkatasivanaga/FuelDeep3D
date-released: '2026-03-04'
contact:
- family-names: Naga
  given-names: Venkata Siva Reddy
  email: venkatasivareddy003@gmail.com