PiC-package

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 &amp; Arrizza (2025) <a href="https://hdl.handle.net/20.500.14243/533471">https://hdl.handle.net/20.500.14243/533471</a>. For single tree segmentation details, see Ferrara et al. (2018) tools:::Rd_expr_doi("10.1016/j.agrformet.2018.04.008").

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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>.

Roberto Ferrara

Pointcloud Interactive Computation

Stefano Arrizza

PiC-package function

Maintainer: Roberto Ferrara <a href="/link/roberto.ferrara%40cnr.it?package=PiC&version=1.2.7" data-mini-rdoc="PiC::roberto.ferrara@cnr.it">roberto.ferrara@cnr.it</a> (<a href="https://orcid.org/0009-0000-3627-6867">ORCID</a>)
Other contributors:<ul>
<li>Stefano Arrizza (<a href="https://orcid.org/0009-0009-2290-3650">ORCID</a>) [contributor]</li>
</ul>

Author

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 &amp; Arrizza (2025) <a href='https://hdl.handle.net/20.500.14243/533471'>https://hdl.handle.net/20.500.14243/533471</a>. For single tree segmentation details, see Ferrara et al. (2018) tools:::Rd_expr_doi("10.1016/j.agrformet.2018.04.008").

PiC: Pointcloud Interactive Computation — PiC-package

Maintainer: Roberto Ferrara <a href='mailto:roberto.ferrara@cnr.it'>roberto.ferrara@cnr.it</a> (<a href='https://orcid.org/0009-0000-3627-6867'>ORCID</a>)
Other contributors:<ul>
<li>Stefano Arrizza (<a href='https://orcid.org/0009-0009-2290-3650'>ORCID</a>) [contributor]</li>
</ul>

PiC-package: PiC: Pointcloud Interactive Computation

Description

Arguments

Author

See Also