.sits_gc_brick

Data cube from where data is to be retrieved.

cube

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

img_col

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

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

cloud_mask

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.

Gilberto Camara

sits

.sits_gc_brick: Create a raster_cube object

Description

Usage

Arguments

Value