PiC: Interactive Processing and Segmentation of Forest TLS
Point-Cloud Data
Tools for the processing, segmentation, and analysis of terrestrial laser
scanning (TLS and MLS) forest point-cloud data. The package provides fast
voxel-based processing, classification of point clouds into forest
floor, understory, canopy, and woody components, and algorithms for
single-tree analysis and structural characterization. Methods are designed
to handle large and dense point-cloud datasets efficiently, supporting
applications in forest structure assessment, connectivity analysis, and
fire-risk evaluation. Input data are provided as '.xyz', '.txt', '.las', or '.laz' point-cloud files.
For methodological details, see Ferrara and Arrizza (2025)
<https://hdl.handle.net/20.500.14243/533471> and Ferrara et al. (2018)
<doi:10.1016/j.agrformet.2018.04.008>.
| Version: |
3.3 |
| Depends: |
R (≥ 4.3) |
| Imports: |
collapse, conicfit, data.table, dbscan, dplyr, magrittr, RANN, stats, tictoc, terra, tools, utils |
| Suggests: |
DT, fs, ggplot2, lidR, grid, gridExtra, later, plotly, shiny, shinycssloaders, shinydashboard, shinydashboardPlus, shinyFeedback, shinyFiles, shinyjs, shinythemes, scales, shinyWidgets, testthat (≥ 3.0.0), tidyr, viridis, withr |
| Published: |
2026-06-27 |
| DOI: |
10.32614/CRAN.package.PiC |
| Author: |
Roberto Ferrara 
[aut, cre],
Stefano Arrizza
[ctb] |
| 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 |
| Additional_repositories: |
https://r-lidar.r-universe.dev |
| Materials: |
README, NEWS |
| CRAN checks: |
PiC results |
Documentation:
Downloads:
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