.sits_tibble_prune

<p>Either a sits tibble or a raster metadata.</p>

data

<p>This function tests if all time series in a sits tibble
have the same number of samples, and returns a time series whose indices
match the majority of the samples.</p>

internal

A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Builds data cubes from collections in AWS, Brazil Data Cube, and Digital Earth Africa.
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

Lorena Santos

Alexandre Carvalho

Pedro Andrade

Karine Ferreira

Alber Sanchez

Gilberto Queiroz

.sits_tibble_prune function

Checks that the timeline of all time series of a data set are equal — .sits_tibble_prune

Checks that the timeline of all time series of a data set are equal

.sits_tibble_prune: Checks that the timeline of all time series of a data set are equal

Description

Usage

Arguments

Value