sits_parallel_fault_tolerant

.sits_parallel_recv_one_data

.sits_parallel_recv_one_result

.sits_parallel_cluster_apply

a given list to be passed to a function

a function to be applied to each list element

additional arguments to fn function

Fault tolerant version of some parallel functions

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

sits_parallel_fault_tolerant: Fault tolerant version of some parallel functions

Description

Usage

Arguments