deforestable: Classify RGB Images into Forest or Non-Forest

Implements two out-of box classifiers presented in <doi:10.48550/arXiv.2112.01063> for distinguishing forest and non-forest terrain images. Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set.

Version: 3.0.0
Depends: R (≥ 4.1.0)
Imports: terra, jpeg, plyr, StableEstim
Published: 2022-04-13
Author: Jesper Muren ORCID iD [aut], Dmitry Otryakhin ORCID iD [aut, cre]
Maintainer: Dmitry Otryakhin <d.otryakhin.acad at protonmail.ch>
License: GPL-3
NeedsCompilation: no
SystemRequirements: C++11, GDAL (>= 2.2.3), GEOS (>= 3.4.0), PROJ (>= 4.9.3), sqlite3
CRAN checks: deforestable results

Documentation:

Reference manual: deforestable.pdf

Downloads:

Package source: deforestable_3.0.0.tar.gz
Windows binaries: r-devel: deforestable_3.0.0.zip, r-release: deforestable_3.0.0.zip, r-oldrel: deforestable_3.0.0.zip
macOS binaries: r-release (arm64): deforestable_3.0.0.tgz, r-oldrel (arm64): deforestable_3.0.0.tgz, r-release (x86_64): deforestable_3.0.0.tgz, r-oldrel (x86_64): deforestable_3.0.0.tgz

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