Package: PiC 1.2.7

PiC: Pointcloud Interactive Computation

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>.

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

PiC_1.2.7.tar.gz
PiC_1.2.7.zip(r-4.7)PiC_1.2.7.zip(r-4.6)PiC_1.2.7.zip(r-4.5)
PiC_1.2.7.tgz(r-4.6-any)PiC_1.2.7.tgz(r-4.5-any)
PiC_1.2.7.tar.gz(r-4.7-any)PiC_1.2.7.tar.gz(r-4.6-any)
PiC_1.2.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
PiC/json (API)
NEWS

# 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:

Conda:

3.06 score 23 scripts 125 downloads 6 exports 37 dependencies

Last updated from:8a09fd7926. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR266
source / vignettesOK201
linux-release-x86_64ERROR185
macos-release-arm64ERROR232
macos-oldrel-arm64ERROR218
windows-develERROR262
windows-releaseERROR299
windows-oldrelERROR280
wasm-releaseOK137

Exports:Calculate_trees_metricsFlosegForest_segrun_PiCSegOneVoxels

Dependencies:classclassIntclicodetoolscollapseconicfitdata.tableDBIdbscandplyre1071foreachgeigengenericsglueiteratorsKernSmoothlifecyclemagrittrMASSpillarpkgconfigpracmaproxyR6Rcpprlangs2sftibbletictoctidyselectunitsutf8vctrswithrwk