.sits_distances

<p>A tibble with time series data and metadata.</p>

data

<p>This function allows using a set of labelled time series as
input to the machine learning models. The attributes used to train the model
are the series themselves. It extracts the time series from a sits tibble
and "spreads" them in time to produce a tibble with distances.</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_distances function

Use time series values as distances for training patterns — .sits_distances

Use time series values as distances for training patterns

.sits_distances: Use time series values as distances for training patterns

Description

Usage

Arguments

Value