TreeLS: Terrestrial Point Cloud Processing of Forest Data

Algorithms for tree detection, noise removal, stem modelling, 3D visualization and manipulation of terrestrial 'LiDAR' (but not only) point clouds, currently focusing on high performance applications for forest inventory - being fully compatible with the 'LAS' infrastructure provided by 'lidR'. For in depth descriptions of the stem classification and segmentation algorithms check out Conto et al. (2017) <doi:10.1016/j.compag.2017.10.019>.

Version: 1.0
Depends: R (≥ 3.3.0), data.table (≥ 1.12.0), magrittr (≥ 1.5), lidR (≥ 2.0.0)
Imports: rgl (≥ 0.99.0), raster (≥ 2.8.19)
LinkingTo: Rcpp, BH, RcppEigen
Published: 2019-03-13
Author: Tiago de Conto [aut, cre]
Maintainer: Tiago de Conto <ti at forlidar.com.br>
License: GPL-3
URL: https://github.com/tiagodc/TreeLS
NeedsCompilation: yes
Materials: README
CRAN checks: TreeLS results

Downloads:

Reference manual: TreeLS.pdf
Package source: TreeLS_1.0.tar.gz
Windows binaries: r-devel: TreeLS_1.0.zip, r-devel-gcc8: TreeLS_1.0.zip, r-release: TreeLS_1.0.zip, r-oldrel: TreeLS_1.0.zip
OS X binaries: r-release: TreeLS_1.0.tgz, r-oldrel: TreeLS_1.0.tgz

Reverse dependencies:

Reverse imports: viewshed3d

Linking:

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