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>.
| 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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