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PiC: Pointcloud Interactive Computation for Forest Structure Analysis
Provides advanced algorithms for analyzing pointcloud data in
forestry applications. Key features include fast voxelization of
large datasets; segmentation of point clouds into forest floor,
understorey, canopy, and wood components. The package enables
efficient processing of large-scale forest pointcloud data, offering
insights into forest structure, connectivity, and fire risk
assessment. Algorithms to analyze pointcloud data (.xyz input file).
For more details, see Ferrara & Arrizza (2025) <https://hdl.handle.net/20.500.14243/533471>.
For single tree segmentation details, see Ferrara et al. (2018)
<doi:10.1016/j.agrformet.2018.04.008>.
| Version: |
1.0.3 |
| Depends: |
R (≥ 4.3) |
| Imports: |
collapse, data.table, dbscan, dplyr, foreach, magrittr, stats, tictoc |
| Suggests: |
ggplot2, testthat (≥ 3.0.0), withr |
| Published: |
2025-02-18 |
| DOI: |
10.32614/CRAN.package.PiC |
| Author: |
Roberto Ferrara 
[aut, cre],
Stefano Arrizza [aut] |
| Maintainer: |
Roberto Ferrara <roberto.ferrara at cnr.it> |
| BugReports: |
https://github.com/rupppy/PiC/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/rupppy/PiC |
| NeedsCompilation: |
no |
| CRAN checks: |
PiC results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=PiC
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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