Package: PiC 1.0.3

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>.

Authors:Roberto Ferrara [aut, cre], Stefano Arrizza [aut]

PiC_1.0.3.tar.gz
PiC_1.0.3.zip(r-4.5)PiC_1.0.3.zip(r-4.4)PiC_1.0.3.zip(r-4.3)
PiC_1.0.3.tgz(r-4.5-any)PiC_1.0.3.tgz(r-4.4-any)PiC_1.0.3.tgz(r-4.3-any)
PiC_1.0.3.tar.gz(r-4.5-noble)PiC_1.0.3.tar.gz(r-4.4-noble)
PiC_1.0.3.tgz(r-4.4-emscripten)PiC_1.0.3.tgz(r-4.3-emscripten)
PiC.pdf |PiC.html✨
PiC/json (API)

# Install 'PiC' in R:
install.packages('PiC', repos = c('https://rupppy.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rupppy/pic/issues

On CRAN:

3.88 score 19 scripts 5 exports 24 dependencies

Last updated 2 days agofrom:b9bfc7d229. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 19 2025
R-4.5-winOKFeb 19 2025
R-4.5-macOKFeb 19 2025
R-4.5-linuxOKFeb 19 2025
R-4.4-winOKFeb 19 2025
R-4.4-macOKFeb 19 2025
R-4.3-winOKFeb 19 2025
R-4.3-macOKFeb 19 2025

Exports:FlosegForest_segSegOneVoxelsWoodseg

Dependencies:clicodetoolscollapsedata.tabledbscandplyrfansiforeachgenericsglueiteratorslifecyclemagrittrpillarpkgconfigR6Rcpprlangtibbletictoctidyselectutf8vctrswithr