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Classifies a block of data using multicores. It breaks the data into horizontal blocks and divides them between the available cores.
Reads data using terra, cleans the data for NAs and missing values. The clean data is stored in a data table with the time instances for all pixels of the block. The algorithm then classifies data on an year by year basis. For each year, extracts the sub-blocks for each band.
After all cores process their blocks, it joins the result and then writes it in the classified images for each corresponding year.
.sits_classify_multicores(
cube,
ml_model,
name,
roi,
filter,
impute_fn,
memsize,
multicores,
output_dir,
version,
.verbose
)data cube.
model trained by sits_train.
name of the output data cube
region of interest
smoothing filter to be applied to the data.
impute function to replace NA
memory available for classification (in GB).
number of cores.
output directory
version of result
print information about processing steps
List of the classified raster layers.