.sits_gc_compose

Data cube from where data is to be retrieved.

cube

A <code>list</code> 'cube_view' with values from cube.

cv_list

A <code>object</code> 'image_collection' containing information
about the images metadata.

img_col

A <code>character</code> with the path and name where the
database will be create. E.g. "my/path/gdalcubes.db"

path_db

A <code>character</code> with the path where the aggregated
images will be writed.

path_images

A <code>logical</code> corresponds to the use of the cloud band
for aggregation.

cloud_mask

Additional parameters that can be included. See
'?gdalcubes::write_tif'.

A <code>character</code> with version of the output files.

version

Save the images based on an aggregation method.

internal

A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Supports data retrieval from a web time series service (WTSS), raster bricks and cubes from the Brazilan Data Cube.
Provides different visualisation methods for image time series.
Provides smoothing methods for noisy time series.
Includes functions for smoothing based on Bayesian inference.
Enables different clustering methods, including dendrograms and self-organized maps (SOM).
Matches noiseless patterns with noisy time series using the time-weighted dynamic time warping (TWDTW) method for shape recognition.
Provides machine learning methods for time series classification, including support vector machines (SVM), random forests and deep learning.
Minimum recommended requirements: 8 GB RAM and 1 CPU dual-core.

.sits_gc_compose: Save the images based on an aggregation method.

Description

Usage

Arguments

Value