sits (version 1.5.3)

sits_cube.results_cube: Create a results cube from local files

Description

Creates a data cube from local files produced by sits operations that produces results (such as probs_cubs and class_cubes)

Usage

# S3 method for results_cube
sits_cube(
  source,
  collection,
  ...,
  data_dir,
  tiles = NULL,
  bands,
  labels = NULL,
  parse_info = c("X1", "X2", "tile", "start_date", "end_date", "band", "version"),
  version = "v1",
  delim = "_",
  multicores = 2L,
  progress = TRUE
)

Value

A tibble describing the contents of a data cube.

Arguments

source

Data source: one of "AWS", "BDC", "CDSE", "DEAFRICA", "DEAUSTRALIA", "HLS", "PLANETSCOPE", "MPC", "SDC" or "USGS". This is the source from which the original data has been downloaded.

collection

Image collection in data source from which the original data has been downloaded. To find out the supported collections, use sits_list_collections()).

...

Other parameters to be passed for specific types.

data_dir

Local directory where images are stored

tiles

Tiles from the collection to be included in the cube.

bands

Results bands to be retrieved ("probs", "bayes", "variance", "class", "uncertainty")

labels

Named vector with labels associated to the classes

parse_info

Parsing information for local files (see notes below).

version

Version of the classified and/or labelled files.

delim

Delimiter for parsing local results cubes (default = "_")

multicores

Number of workers for parallel processing (integer, min = 1, max = 2048).

progress

Logical: show a progress bar?

Examples

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 = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube, ml_model = rfor_model, output_dir = tempdir()
    )
    # plot the probability cube
    plot(probs_cube)

    # obtain and name the labels of the local probs cube
    labels <- sits_labels(rfor_model)
    names(labels) <- seq_along(labels)

    # recover the local probability cube
    probs_local_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = tempdir(),
        bands = "probs",
        labels = labels
    )
    # compare the two plots (they should be the same)
    plot(probs_local_cube)

    # smooth the probability cube using Bayesian statistics
    bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
    # plot the smoothed cube
    plot(bayes_cube)

    # recover the local smoothed cube
    smooth_local_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = tempdir(),
        bands = "bayes",
        labels = labels
    )
    # compare the two plots (they should be the same)
    plot(smooth_local_cube)

    # label the probability cube
    label_cube <- sits_label_classification(
        bayes_cube,
        output_dir = tempdir()
    )
    # plot the labelled cube
    plot(label_cube)

    # recover the local classified cube
    class_local_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = tempdir(),
        bands = "class",
        labels = labels
    )
    # compare the two plots (they should be the same)
    plot(class_local_cube)

    # obtain an uncertainty cube with entropy
    entropy_cube <- sits_uncertainty(
        cube = bayes_cube,
        type = "entropy",
        output_dir = tempdir()
    )
    # plot entropy values
    plot(entropy_cube)

    # recover an uncertainty cube with entropy
    entropy_local_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = tempdir(),
        bands = "entropy"
    )
    # plot recovered entropy values
    plot(entropy_local_cube)

    # obtain an uncertainty cube with margin
    margin_cube <- sits_uncertainty(
        cube = bayes_cube,
        type = "margin",
        output_dir = tempdir()
    )
    # plot entropy values
    plot(margin_cube)

    # recover an uncertainty cube with entropy
    margin_local_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = tempdir(),
        bands = "margin"
    )
    # plot recovered entropy values
    plot(margin_local_cube)
}