PiC: Pointcloud Interactive Computation (original) (raw)

Provides advanced algorithms for analyzing pointcloud data from terrestrial laser scanner 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:	1.2.7
Depends:	R (≥ 4.3)
Imports:	collapse, conicfit, data.table, dbscan, dplyr, foreach, magrittr, sf, stats, tictoc, utils
Suggests:	DT, fs, ggplot2, later, plotly, shiny, shinycssloaders, shinydashboard, shinydashboardPlus, shinyFeedback, shinyFiles, shinyjs, shinythemes, shinyWidgets, testthat (≥ 3.0.0), tools, withr
Published:	2025-11-07
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
Materials:	README, NEWS
CRAN checks:	PiC results

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