sits (version 1.5.3)

sits_cube.vector_cube: Create a vector cube from local files

Description

Creates a data cube from local files which include a vector file produced by a segmentation algorithm.

Usage

# S3 method for vector_cube
sits_cube(
  source,
  collection,
  ...,
  raster_cube,
  vector_dir,
  vector_band,
  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 data has been downloaded.

collection

Image collection in data source. To find out the supported collections, use sits_list_collections()).

...

Other parameters to be passed for specific types.

raster_cube

Raster cube to be merged with vector data

vector_dir

Local directory where vector files are stored

vector_band

Band for vector cube ("segments", "probs", "class")

parse_info

Parsing information for local image files

version

Version of the classified and/or labelled files.

delim

Delimiter for parsing local files (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 cube based on a local MODIS data
    # MODIS local files have names such as
    # "TERRA_MODIS_012010_NDVI_2013-09-14.jp2"
    # see the parse info parameter as an example on how to
    # decode local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    modis_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir,
        parse_info = c("satellite", "sensor", "tile", "band", "date")
    )
    # segment the vector cube
    segs_cube <- sits_segment(
        cube = modis_cube,
        seg_fn = sits_slic(
            step = 10,
            compactness = 1,
            dist_fun = "euclidean",
            avg_fun = "median",
            iter = 30,
            minarea = 10
        ),
        output_dir = tempdir()
    )
    plot(segs_cube)

    # recover the local segmented cube
    local_segs_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        raster_cube = modis_cube,
        vector_dir = tempdir(),
        vector_band = "segments"
    )
    # plot the recover model and compare
    plot(local_segs_cube)

    # classify the segments
    # create a random forest model
    rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
    probs_vector_cube <- sits_classify(
        data = segs_cube,
        ml_model = rfor_model,
        output_dir = tempdir(),
        n_sam_pol = 10
    )
    plot(probs_vector_cube)

    # recover vector cube
    local_probs_vector_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        raster_cube = modis_cube,
        vector_dir = tempdir(),
        vector_band = "probs"
    )
    plot(local_probs_vector_cube)

    # label the segments
    class_vector_cube <- sits_label_classification(
        cube = probs_vector_cube,
        output_dir = tempdir(),
    )
    plot(class_vector_cube)

    # recover vector cube
    local_class_vector_cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        raster_cube = modis_cube,
        vector_dir = tempdir(),
        vector_band = "class"
    )
    plot(local_class_vector_cube)
}