mlr3spatial

Package website: release | dev

mlr3spatial is an extension package for spatial objects within the mlr3 ecosystem.

Feature Overview

• Read training data from sf objects.
• Predict on raster objects from the packages terra, raster and stars.
• Write model predictions to raster files.
• Predict large raster objects in parallel.
• Read raster objects in chunks to avoid memory issues.
• Built-in toy tasks leipzig.

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

# load 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 predict task
task_predict = as_task_unsupervised(leipzig_raster)

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

plot(land_cover)

FAQ