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. 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. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
Version: 1.5.2 Depends: R (≥ 4.1.0) Imports: yaml (≥ 2.3.0), dplyr (≥ 1.1.0), grDevices, graphics, leaflet (≥ 2.2.2), lubridate, luz (≥ 0.4.0), parallel, purrr (≥ 1.0.2), randomForest, Rcpp (≥ 1.0.13), rstac (≥ 1.0.1), sf (≥ 1.0-19), slider (≥ 0.2.0), stats, terra (≥ 1.8-5), tibble (≥ 3.1), tidyr (≥ 1.3.0), tmap (≥ 4.0), torch (≥ 0.14.0), 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), methods, mgcv, nnet, openxlsx, proxy, randomForestExplainer, RColorBrewer, RcppArmadillo (≥ 0.12), scales, spdep, stringr, supercells (≥ 1.0.0), testthat (≥ 3.1.3), tools, xgboost Published: 2025-02-12 DOI: 10.32614/CRAN.package.sits Author: Rolf Simoes [aut], Gilberto Camara [aut, cre, ths], Felipe Souza [aut], Felipe Carlos [aut], Lorena Santos [ctb], Karine Ferreira [ctb, ths], Charlotte Pelletier [ctb], Pedro Andrade [ctb], Alber Sanchez [ctb], Estefania Pizarro [ctb], Gilberto Queiroz [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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sits to link to this page.
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