landmap: landmap: Automated Spatial Prediction using Ensemble Machine...
In landmap: Automated Spatial Prediction using Ensemble Machine Learning
Description
Geographical distances can be used with remote sensing covariates and process-based derivatives
to improve spatial prediction and/or interpolation from point data. This package shows how to
fully automate process so that predictions and model errors can be generated using unbiased
estimation (train.spLearner package). Additional functions are used to access global layers
(from www.openlandmap.org), to process large rasters (spatial tiling) including running own
customized functions in parallel.
landmap documentation built on Oct. 14, 2021, 5:24 p.m.
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Description
Geographical distances can be used with remote sensing covariates and process-based derivatives to improve spatial prediction and/or interpolation from point data. This package shows how to fully automate process so that predictions and model errors can be generated using unbiased estimation (train.spLearner package). Additional functions are used to access global layers (from www.openlandmap.org), to process large rasters (spatial tiling) including running own customized functions in parallel.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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