.sits_s2_l2a_aws_tile_cube

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<p>Tibble with information about the files.</p>

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A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Supports data retrieval from a web time series service (WTSS), raster bricks and cubes from the Brazilan Data Cube.
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.

Gilberto Camara

sits

Satellite Image Time Series Analysis for Remote Sensing Data Cubes

Rolf Simoes

Felipe Souza

Pedro Andrade

Lorena Santos

Alexandre Carvalho

Gilberto Queiroz

Karine Ferreira

Alber Sanchez

Victor Maus

.sits_s2_l2a_aws_tile_cube function

Create a data cube for a Sentinel-2 AWS TILE — .sits_s2_l2a_aws_tile_cube

Create a data cube for a Sentinel-2 AWS TILE

.sits_s2_l2a_aws_tile_cube: Create a data cube for a Sentinel-2 AWS TILE

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