.sits_gc_compose

tile

name

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

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

img_col

Database to be created by gdalcubes

db_file

Directory where the aggregated images will be written.

dir_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.
Builds data cubes from collections in AWS, Brazil Data Cube, and Digital Earth Africa.
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