sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

An end-to-end toolkit for land use and land cover classification using big Earth observation data. Builds satellite image data cubes from cloud collections. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Enables merging of multi-source imagery (SAR, optical, DEM). Includes functions for quality assessment of training samples using self-organized maps and to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolution neural networks, and temporal attention encoders. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference. Enables best practices for estimating area and assessing accuracy of land change. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Version:	1.5.3
Depends:	R (≥ 4.1.0)
Imports:	yaml (≥ 2.3.0), dplyr (≥ 1.1.0), grDevices, graphics, leafgl, leaflet (≥ 2.2.2), lubridate, luz (≥ 0.4.0), parallel, purrr (≥ 1.0.2), randomForest, Rcpp (≥ 1.1.0), rstac (≥ 1.0.1), sf (≥ 1.0-19), slider (≥ 0.2.0), stats, terra (≥ 1.8-54), tibble (≥ 3.3.0), tidyr (≥ 1.3.0), tmap (≥ 4.1), torch (≥ 0.15.0), units, utils
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	aws.s3, caret, cli, cols4all (≥ 0.8.0), covr, dendextend, dtwclust, DiagrammeR, digest, e1071, exactextractr, FNN, gdalcubes (≥ 0.7.0), geojsonsf, ggplot2, httr2 (≥ 1.1.0), jsonlite, kohonen (≥ 3.0.11), lightgbm, methods, mgcv, nnet, openxlsx, proxy, randomForestExplainer, RColorBrewer, RcppArmadillo (≥ 14.0.0), scales, spdep, stars, stringr, supercells (≥ 1.0.0), testthat (≥ 3.1.3), tools, xgboost
Published:	2025-07-23
DOI:	10.32614/CRAN.package.sits
Author:	Rolf Simoes [aut], Gilberto Camara [aut, cre, ths], Felipe Souza [aut], Felipe Carlos [aut], Lorena Santos [ctb], Charlotte Pelletier [ctb], Estefania Pizarro [ctb], Karine Ferreira [ctb, ths], Alber Sanchez [ctb], Alexandre Assuncao [ctb], Daniel Falbel [ctb], Gilberto Queiroz [ctb], Johannes Reiche [ctb], Pedro Andrade [ctb], Pedro Brito [ctb], Renato Assuncao [ctb], Ricardo Cartaxo [ctb]
Maintainer:	Gilberto Camara <gilberto.camara.inpe at gmail.com>
BugReports:	https://github.com/e-sensing/sits/issues
License:	GPL-2
URL:	https://github.com/e-sensing/sits/, https://e-sensing.github.io/sitsbook/
NeedsCompilation:	yes
Language:	en-US
Citation:	sits citation info
Materials:	NEWS
In views:	Spatial
CRAN checks:	sits results

Documentation:

Reference manual:

sits.html , sits.pdf

Downloads:

Package source:	sits_1.5.3.tar.gz
Windows binaries:	r-devel: sits_1.5.3.zip, r-release: sits_1.5.3.zip, r-oldrel: sits_1.5.3.zip
macOS binaries:	r-release (arm64): sits_1.5.3.tgz, r-oldrel (arm64): sits_1.5.3.tgz, r-release (x86_64): sits_1.5.3.tgz, r-oldrel (x86_64): sits_1.5.3.tgz
Old sources:	sits archive

Linking:

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