sits_predictors

Predictors are X-Y values required for machine learning
algorithms, organized as a data table where each row corresponds
to a training sample. The first two columns of the predictors table
are categorical (<code>label_id</code> and <code>label</code>). The other columns are
the values of each band and time, organized first by band and then by time.

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) <doi:10.48550/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_predictors function

<dl><dt>samples</dt>
<dd>Time series in sits format (tibble of class "sits")</dd></dl>

Arguments

Gilberto Camara, <a href="/link/gilberto.camara%40inpe.br?package=sits&version=1.5.0" data-mini-rdoc="sits::gilberto.camara@inpe.br">gilberto.camara@inpe.br</a>

Author

Obtain predictors for time series samples — sits_predictors

<dl>

<dt>samples</dt>
<dd>Time series in sits format (tibble of class "sits")</dd>

</dl>

Gilberto Camara, <a href='mailto:gilberto.camara@inpe.br'>gilberto.camara@inpe.br</a>

sits_predictors: Obtain predictors for time series samples

Description

Usage

Value

Arguments

Author

Examples