sits_cube_copy: Copy the images of a cube to a local directory

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Copy the images of a cube to a local directory

Description

This function downloads the images of a cube in parallel. A region of interest (roi) can be provided to crop the images and a resolution (res) to resample the bands. sits_cube_copy is useful to improve processing time in the regularization operation.

Usage

sits_cube_copy(
  cube,
  roi = NULL,
  res = NULL,
  crs = NULL,
  n_tries = 3L,
  multicores = 2L,
  output_dir,
  progress = TRUE
)

Arguments

cube	A data cube (class "raster_cube")
roi	Region of interest. Either: A path to a shapefile with polygons; A sf object from sf package; A named vector ("lon_min", "lat_min", "lon_max", "lat_max") in WGS84; A named vector ("xmin", "xmax", "ymin", "ymax") with XY coordinates in WGS84.
res	An integer value corresponds to the output spatial resolution of the images. Default is NULL.
crs	The Coordinate Reference System (CRS) of the roi. (see details below).
n_tries	Number of attempts to download the same image. Default is 3.
multicores	Number of cores for parallel downloading (integer, min = 1, max = 2048).
output_dir	Output directory where images will be saved. (character vector of length 1).
progress	Logical: show progress bar?

Value

Copy of input data cube (class "raster cube").

The main sits classification workflow has the following steps:

1. sits_cube: selects a ARD image collection from a cloud provider.

2. sits_cube_copy: copies an ARD image collection from a cloud provider to a local directory for faster processing.

3. sits_regularize: create a regular data cube from an ARD image collection.

4. sits_apply: create new indices by combining bands of a regular data cube (optional).

5. sits_get_data: extract time series from a regular data cube based on user-provided labelled samples.

6. sits_train: train a machine learning model based on image time series.

7. sits_classify: classify a data cube using a machine learning model and obtain a probability cube.

8. sits_smooth: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.

9. sits_label_classification: produce a classified map by selecting the label with the highest probability from a smoothed cube.

The roi parameter is used to crop cube images. To define a roi use one of:

• A path to a shapefile with polygons;

• A sfc or sf object from sf package;

• A SpatExtent object from terra package;

• A named vector ("lon_min", "lat_min", "lon_max", "lat_max") in WGS84;

• A named vector ("xmin", "xmax", "ymin", "ymax") with XY coordinates.

Defining a region of interest using SpatExtent or XY values not in WGS84 requires the crs parameter to be specified.

Author(s)

Felipe Carlos, [e-mail]

Felipe Carvalho, [e-mail]

Examples

if (sits_run_examples()) {
    # Creating a sits cube from BDC
    bdc_cube <- sits_cube(
        source = "BDC",
        collection = "CBERS-WFI-16D",
        tiles = c("007004", "007005"),
        bands = c("B15", "CLOUD"),
        start_date = "2018-01-01",
        end_date = "2018-01-12"
    )
    # Downloading images to a temporary directory
    cube_local <- sits_cube_copy(
        cube = bdc_cube,
        output_dir = tempdir(),
        roi = c(
            lon_min = -46.5,
            lat_min = -45.5,
            lon_max = -15.5,
            lat_max = -14.6
        ),
        multicores = 2L,
        res = 250
    )
}

sits documentation built on Nov. 6, 2025, 1:15 a.m.