sits_regularize

Produces regular data cubes for analysis-ready data (ARD)
image collections. Analysis-ready data (ARD) collections available in
AWS, MPC, USGS and DEAfrica are not regular in space and time.
Bands may have different resolutions,
images may not cover the entire time, and time intervals are not regular.
For this reason, subsets of these collection need to be converted to
regular data cubes before further processing and data analysis.
This function requires users to include the cloud band in their ARD-based
data cubes.

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_regularize function

<dl><dt>cube</dt>
<dd><code>raster_cube</code> object whose observation
period and/or spatial resolution is not constant.</dd>
<dt>period</dt>
<dd>ISO8601-compliant time period for regular
data cubes, with number and unit, where
"D", "M" and "Y" stand for days, month and year;
 e.g., "P16D" for 16 days.</dd>
<dt>res</dt>
<dd>Spatial resolution of regularized images (in meters).</dd>
<dt>output_dir</dt>
<dd>Valid directory for storing regularized images.</dd>
<dt>roi</dt>
<dd>A named <code>numeric</code> vector with a region of interest.
See more above.</dd>
<dt>multicores</dt>
<dd>Number of cores used for regularization;
used for parallel processing of input.</dd>
<dt>progress</dt>
<dd>show progress bar?</dd></dl>

Arguments

Build a regular data cube from an irregular one — sits_regularize

<dl>

<dt>cube</dt>
<dd><code>raster_cube</code> object whose observation
period and/or spatial resolution is not constant.</dd>


<dt>period</dt>
<dd>ISO8601-compliant time period for regular
data cubes, with number and unit, where
"D", "M" and "Y" stand for days, month and year;
 e.g., "P16D" for 16 days.</dd>


<dt>res</dt>
<dd>Spatial resolution of regularized images (in meters).</dd>


<dt>output_dir</dt>
<dd>Valid directory for storing regularized images.</dd>


<dt>roi</dt>
<dd>A named <code>numeric</code> vector with a region of interest.
See more above.</dd>


<dt>multicores</dt>
<dd>Number of cores used for regularization;
used for parallel processing of input.</dd>


<dt>progress</dt>
<dd>show progress bar?</dd>

</dl>

sits_regularize: Build a regular data cube from an irregular one

Description

Usage

Value

Arguments

References

Examples