source_collection

.source_collections

.source_collection_access_test

.source_collection_access_vars_set

.source_collection_check

.source_collection_metadata_search

.source_collection_name

.source_collection_open_data

.source_collection_open_data_token

.source_collection_token_check

.source_collection_tile_check

These functions provide an API to handle/retrieve data from source's
collections.
<code>.source_collections()</code> lists all collections of a source.
<code>.source_collection_access_vars_set</code> sets
<code>access_vars</code> environment variables.
<code>.source_collection_check()</code> checks if a collection
is from a source.
<code>.source_collection_metadata_search()</code> retrieves the
metadata search strategy for a given source and collection.
<code>.source_collection_name()</code> returns the name of a
collection in its original source.
<code>.source_collection_open_data()</code> informs if a
collection is open data or not.
<code>.source_collection_open_data_token()</code> informs if a
collection requires a token to access.
<code>.source_collection_token_check()</code> checks if a collection
needs environmental variables.
<code>.source_collection_tile_check()</code> checks if a collection
requires tiles to be defined

internal

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 STAC protocol <https://stacspec.org/>
and the 'gdalcubes' R package <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 <doi:10.3390/rs11050523>,
residual networks <arxiv:1809.04356>, and temporal attention encoders
<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

source_collection function

<dl><dt>source</dt>
<dd>Data source.</dd>
<dt>collection</dt>
<dd>Image collection.</dd>
<dt>tiles</dt>
<dd>Tile names</dd>
<dt>start_date</dt>
<dd>Start date.</dd>
<dt>end_date</dt>
<dd>End date.</dd></dl>

Arguments

Source collection functions — source_collection

<dl>

<dt>source</dt>
<dd>Data source.</dd>


<dt>collection</dt>
<dd>Image collection.</dd>


<dt>tiles</dt>
<dd>Tile names</dd>


<dt>start_date</dt>
<dd>Start date.</dd>


<dt>end_date</dt>
<dd>End date.</dd>

</dl>

Source collection functions

source_collection: Source collection functions

Description

Usage

Value

Arguments