.source_items_cube

Data source (one of "SATVEG", "LOCAL", "BDC", "AWS",
"USGS", "DEAFRICA", "PROBS").

source

Collection to be searched in the data source.

collection

A <code>character</code> with cube name.

name

A <code>STACItemCollection</code> object returned by rstac or
<code>character</code> vector with directories to be search.

items

A <code>tibble</code> with informations about datetime, bands,
res, and path.

file_info

Create a sits cube object for tile, at the end the tiles are
 merged to form a single object.

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.

.source_items_cube: Create a cube object for each tile

Description

Usage

Arguments

Value