sits_cube_copy {sits} | R Documentation |
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:
|
res |
An integer value corresponds to the output spatial resolution of the images. Default is NULL. |
crs |
Reference system for output cube (by default, the same CRS from the input cube is assumed) |
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:
sits_cube
: selects a ARD image collection from a cloud provider.sits_cube_copy
: copies an ARD image collection from a cloud provider to a local directory for faster processing.sits_regularize
: create a regular data cube from an ARD image collection.sits_apply
: create new indices by combining bands of a regular data cube (optional).sits_get_data
: extract time series from a regular data cube based on user-provided labelled samples.sits_train
: train a machine learning model based on image time series.sits_classify
: classify a data cube using a machine learning model and obtain a probability cube.sits_smooth
: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.sits_label_classification
: produce a classified map by selecting the label with the highest probability from a smoothed cube.
Author(s)
Felipe Carlos, efelipecarlos@gmail.com
Felipe Carvalho, felipe.carvalho@inpe.br
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
)
}