sits_configuration

sits_config

sits_config_show

A <code>numeric</code> value to estimate
growth size of R memory relative to block size.

processing_bloat

A <code>numeric</code> value indicating number of
items returned by STAC service.

rstac_pagination_limit

A <code>character</code> value indicating a
supported raster handling package.

raster_api_package

A <code>character</code> vector specifying GDAL
creation option for GeoTiff image format.

gdal_creation_options

A <code>logical</code> value indicating if current
configuration options must be cleaned before
load config files. Default <code>FALSE</code>.

reset

A <code>character</code> value indicating a source
key entry to be shown in detail.

source

A <code>character</code> value used in conjunction
with <code>source</code> parameter to indicate a collection key entry to be shown
in detail.

collection

A <code>character</code> value indicating a palette
to be shown in detail.

palette

These functions load and show sits configurations.<code>sits_config()</code> loads the default configuration file and
the user provided configuration file. The final configuration is
obtained by overriding the options by the values provided in
<code>processing_bloat</code>, <code>rstac_pagination_limit</code>,
<code>raster_api_package</code>, and <code>gdal_creation_options</code> parameters.<code>sits_config_show()</code> prints the current sits
configuration options. To show specific configuration options for
a source, a collection, or a palette, user can inform the corresponding
keys to <code>source</code>, <code>collection</code>, and <code>palette</code> parameters.

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_configuration: sits configuration

Description

Usage

Arguments

Value

Details