FuelDeep3D
0.1.13D Fuel Segmentation Using Terrestrial Laser Scanning and Deep Learning
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-03-1011 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 64%
- Documented parameters
- 96%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
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Checks run against github.com/venkatasivanaga/fueldeep3d on 2026-07-31.
Dependencies
Nothing depends on this yet.
Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-03-02
- Total releases
- 1 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 4.1
- Download size
- 6.7 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet