if (sits_run_examples()) {
# create a random forest model
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6",
data_dir = data_dir
)
# classify a data cube
probs_cube <- sits_classify(
data = cube, ml_model = rfor_model, output_dir = tempdir()
)
# smooth the probability cube using Bayesian statistics
bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
# label the probability cube
label_cube <- sits_label_classification(
bayes_cube,
output_dir = tempdir()
)
# create roi
roi <- sf::st_sfc(
sf::st_polygon(
list(rbind(
c(-55.64768, -11.68649),
c(-55.69654, -11.66455),
c(-55.62973, -11.61519),
c(-55.64768, -11.68649)
))
),
crs = "EPSG:4326"
)
# crop and mosaic classified image
mosaic_cube <- sits_mosaic(
cube = label_cube,
roi = roi,
crs = "EPSG:4326",
output_dir = tempdir()
)
}
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