mlr3spatial

Package website: release | dev

mlr3spatial is the package for spatial objects within the mlr3 ecosystem. The package directly loads data from sf objects to train any mlr3 learner. The learner can predict on various raster formats (terra, raster and stars) and writes the prediction raster to disk. mlr3spatial reads large raster objects in chunks to avoid memory issues and predicts the chunks in parallel. Check out mlr3spatiotempcv for spatiotemporal resampling within mlr3.

Installation

Install the last release from CRAN:

install.packages("mlr3spatial")

Install the development version from GitHub:

remotes::install_github("mlr-org/mlr3spatial")

Example

library(mlr3)
library(mlr3spatial)
library(terra, exclude = "resample")
library(sf)

# load sample points
leipzig_vector = read_sf(system.file("extdata", "leipzig_points.gpkg",
  package = "mlr3spatial"), stringsAsFactors = TRUE)

# create land cover task
task = as_task_classif_st(leipzig_vector, target = "land_cover")
task

## <TaskClassifST:leipzig_vector> (97 x 9)
## * Target: land_cover
## * Properties: multiclass
## * Features (8):
##   - dbl (8): b02, b03, b04, b06, b07, b08, b11, ndvi
## * Coordinates:
##            X       Y
##  1: 732480.1 5693957
##  2: 732217.4 5692769
##  3: 732737.2 5692469
##  4: 733169.3 5692777
##  5: 732202.2 5692644
## ---                 
## 93: 733018.7 5692342
## 94: 732551.4 5692887
## 95: 732520.4 5692589
## 96: 732542.2 5692204
## 97: 732437.8 5692300

# create learner
learner = lrn("classif.rpart")

# train the model
learner$train(task)

# load raster file
leipzig_raster = rast(system.file("extdata", "leipzig_raster.tif", package = "mlr3spatial"))

plotRGB(leipzig_raster, r = 3, g = 2, b = 1)

# create prediction task
task_predict = as_task_unsupervised(leipzig_raster)

# predict land cover map
land_cover = predict_spatial(task_predict, learner)

plot(land_cover, col = c("#440154FF", "#443A83FF", "#31688EFF",
  "#21908CFF", "#35B779FF", "#8FD744FF", "#FDE725FF"))

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