sits_config

These functions load and show sits configurations.
The `sits` package uses a configuration file
that contains information on parameters required by different functions.
This includes information about the image collections handled by `sits`.
<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>gdal_creation_options</code> and <code>gdalcubes_chunk_size</code>

An end-to-end toolkit for land use and land cover classification
using big Earth observation data, based on machine learning methods
applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>.
Builds regular data cubes from collections in AWS, Microsoft Planetary Computer,
Brazil Data Cube, and Digital Earth Africa using the Spatio-temporal Asset Catalog (STAC)
protocol (<https://stacspec.org/> and the 'gdalcubes' R package
developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>.
Supports visualization methods for images and time series and
smoothing filters for dealing with noisy time series.
Includes functions for quality assessment of training samples using self-organized maps
as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>.
Provides machine learning methods including support vector machines,
random forests, extreme gradient boosting, multi-layer perceptrons,
temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>,
residual networks by Fawaz et al (2019) <doi:10.1007/s10618-019-00619-1>, and temporal attention encoders
by Garnot and Landrieu (2020) <arXiv:2007.00586>.
Performs efficient classification of big Earth observation data cubes and includes
functions for post-classification smoothing based on Bayesian inference, and
methods for uncertainty assessment. Enables best
practices for estimating area and assessing accuracy of land change as
recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>.
Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Gilberto Camara

sits

Satellite Image Time Series Analysis for Earth Observation Data
Cubes

Rolf Simoes

Felipe Souza

Lorena Santos

Pedro Andrade

Karine Ferreira

Alber Sanchez

Gilberto Queiroz

sits_config function

<dl><dt>processing_bloat</dt>
<dd>Estimated growth size of R memory relative
to block size.</dd>
<dt>rstac_pagination_limit</dt>
<dd>Limit of number of items returned by STAC.</dd>
<dt>gdal_creation_options</dt>
<dd>GDAL creation options for GeoTiff.</dd>
<dt>gdalcubes_chunk_size</dt>
<dd>Chunk size to be used by gdalcubes</dd>
<dt>reset</dt>
<dd>Should current configuration options be
cleaned before loading config files?
Default is <code>FALSE</code>.</dd></dl>

Arguments

Rolf Simoes, <a href="/link/rolf.simoes%40inpe.br?package=sits&version=1.4.1" data-mini-rdoc="sits::rolf.simoes@inpe.br">rolf.simoes@inpe.br</a>

Author

Configure parameters for sits package — sits_config

<dl>

<dt>processing_bloat</dt>
<dd>Estimated growth size of R memory relative
to block size.</dd>


<dt>rstac_pagination_limit</dt>
<dd>Limit of number of items returned by STAC.</dd>


<dt>gdal_creation_options</dt>
<dd>GDAL creation options for GeoTiff.</dd>


<dt>gdalcubes_chunk_size</dt>
<dd>Chunk size to be used by gdalcubes</dd>


<dt>reset</dt>
<dd>Should current configuration options be
cleaned before loading config files?
Default is <code>FALSE</code>.</dd>

</dl>

Rolf Simoes, <a href='mailto:rolf.simoes@inpe.br'>rolf.simoes@inpe.br</a>

sits_config: Configure parameters for sits package

Description

Usage

Value

Arguments

Author

Details

Examples