Package: sits Type: Package Version: 1.5.4 Title: Satellite Image Time Series Analysis for Earth Observation Data Cubes Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = 'rolfsimoes@gmail.com'), person('Gilberto', 'Camara', role = c('aut', 'cre', 'ths'), email = 'gilberto.camara.inpe@gmail.com'), person('Felipe', 'Souza', role = c('aut'), email = 'felipe.carvalho@inpe.br'), person('Felipe', 'Carlos', role = c('aut'), email = "efelipecarlos@gmail.com"), person('Lorena', 'Santos', role = c('ctb'), email = 'lorena.santos@inpe.br'), person('Charlotte', 'Pelletier', role = c('ctb'), email = 'charlotte.pelletier@univ-ubs.fr'), person('Estefania', 'Pizarro', role = c('ctb'), email = 'eapizarroa@ine.gob.cl'), person('Karine', 'Ferreira', role = c('ctb', 'ths'), email = 'karine.ferreira@inpe.br'), person('Alber', 'Sanchez', role = c('ctb'), email = 'alber.ipia@inpe.br'), person('Alexandre', 'Assuncao', role = c('ctb'), email = 'alexcarssuncao@gmail.com'), person('Daniel', 'Falbel', role = c('ctb'), email = 'dfalbel@gmail.com'), person('Gilberto', 'Queiroz', role = c('ctb'), email = 'gilberto.queiroz@inpe.br'), person('Johannes', 'Reiche', role = c('ctb'), email = 'johannes.reiche@wur.nl'), person('Pedro', 'Andrade', role = c('ctb'), email = 'pedro.andrade@inpe.br'), person('Pedro', 'Brito', role = c('ctb'), email = 'pedro_brito1997@hotmail.com'), person('Renato', 'Assuncao', role = c('ctb'), email = 'assuncaoest@gmail.com'), person('Ricardo', 'Cartaxo', role = c('ctb'), email = 'rcartaxoms@gmail.com') ) Maintainer: Gilberto Camara Description: 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. Encoding: UTF-8 Language: en-US Depends: R (>= 4.1.0) URL: https://github.com/e-sensing/sits/, https://e-sensing.github.io/sitsbook/, https://e-sensing.github.io/sits/ BugReports: https://github.com/e-sensing/sits/issues License: GPL-2 ByteCompile: true LazyData: true Imports: yaml (>= 2.3.0), dplyr (>= 1.1.0), grDevices, graphics, httr2 (>= 1.1.0), 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 (>= 0.16.3), units, utils Suggests: aws.s3, caret, cli, cols4all (>= 0.8.0), covr, dendextend, dtwclust, digest, e1071, exactextractr, FNN, gdalcubes (>= 0.7.0), geojsonsf, ggplot2, jsonlite, kohonen (>= 3.0.11), lightgbm, methods, mgcv, nnet, openxlsx, parallelly, proxy, randomForestExplainer, RColorBrewer, RcppArmadillo (>= 14.0.0), scales, snic, spdep, stars, stringr, supercells (>= 1.0.0), testthat (>= 3.1.3), tools, xgboost Config/testthat/edition: 3 Config/testthat/parallel: false Config/testthat/start-first: cube, raster, regularize, data, ml LinkingTo: Rcpp, RcppArmadillo RoxygenNote: 7.3.3 Collate: 'api_accessors.R' 'api_accuracy.R' 'api_apply.R' 'api_band.R' 'api_bayts.R' 'api_bbox.R' 'api_block.R' 'api_check.R' 'api_chunks.R' 'api_classify.R' 'api_clean.R' 'api_cluster.R' 'api_colors.R' 'api_combine_predictions.R' 'api_comp.R' 'api_conf.R' 'api_crop.R' 'api_csv.R' 'api_cube.R' 'api_data.R' 'api_debug.R' 'api_detect_change.R' 'api_download.R' 'api_dtw.R' 'api_environment.R' 'api_factory.R' 'api_file_info.R' 'api_file.R' 'api_gdal.R' 'api_gdalcubes.R' 'api_grid.R' 'api_jobs.R' 'api_kohonen.R' 'api_label_class.R' 'api_mask.R' 'api_merge.R' 'api_mixture_model.R' 'api_ml_model.R' 'api_message.R' 'api_mosaic.R' 'api_opensearch.R' 'api_parallel.R' 'api_patterns.R' 'api_period.R' 'api_plot_time_series.R' 'api_plot_raster.R' 'api_plot_vector.R' 'api_point.R' 'api_predictors.R' 'api_raster.R' 'api_reclassify.R' 'api_reduce.R' 'api_regularize.R' 'api_request.R' 'api_request_httr2.R' 'api_roi.R' 'api_samples.R' 'api_segments.R' 'api_select.R' 'api_sf.R' 'api_shp.R' 'api_signal.R' 'api_smooth.R' 'api_smote.R' 'api_som.R' 'api_source.R' 'api_source_aws.R' 'api_source_bdc.R' 'api_source_cdse.R' 'api_source_cdse_os.R' 'api_source_deafrica.R' 'api_source_deaustralia.R' 'api_source_hls.R' 'api_source_local.R' 'api_source_mpc.R' 'api_source_ogh.R' 'api_source_sdc.R' 'api_source_stac.R' 'api_source_terrascope.R' 'api_source_usgs.R' 'api_space_time_operations.R' 'api_stac.R' 'api_stats.R' 'api_summary.R' 'api_texture.R' 'api_tibble.R' 'api_tile.R' 'api_timeline.R' 'api_tmap.R' 'api_torch.R' 'api_torch_psetae.R' 'api_ts.R' 'api_tuning.R' 'api_uncertainty.R' 'api_utils.R' 'api_validate.R' 'api_values.R' 'api_variance.R' 'api_vector.R' 'api_vector_info.R' 'api_view.R' 'RcppExports.R' 'data.R' 'sits-package.R' 'sits_add_base_cube.R' 'sits_apply.R' 'sits_accuracy.R' 'sits_bands.R' 'sits_bayts.R' 'sits_bbox.R' 'sits_classify.R' 'sits_colors.R' 'sits_combine_predictions.R' 'sits_config.R' 'sits_csv.R' 'sits_cube.R' 'sits_cube_copy.R' 'sits_cube_local.R' 'sits_clean.R' 'sits_cluster.R' 'sits_detect_change.R' 'sits_detect_change_method.R' 'sits_dtw.R' 'sits_factory.R' 'sits_filters.R' 'sits_geo_dist.R' 'sits_get_data.R' 'sits_get_class.R' 'sits_get_probs.R' 'sits_grid_systems.R' 'sits_histogram.R' 'sits_imputation.R' 'sits_labels.R' 'sits_label_classification.R' 'sits_lighttae.R' 'sits_lstm_fcn.R' 'sits_machine_learning.R' 'sits_merge.R' 'sits_mixture_model.R' 'sits_mlp.R' 'sits_mosaic.R' 'sits_model_export.R' 'sits_patterns.R' 'sits_plot.R' 'sits_predictors.R' 'sits_reclassify.R' 'sits_reduce.R' 'sits_reduce_imbalance.R' 'sits_regularize.R' 'sits_resnet.R' 'sits_sample_functions.R' 'sits_segmentation.R' 'sits_select.R' 'sits_sf.R' 'sits_smooth.R' 'sits_som.R' 'sits_stars.R' 'sits_summary.R' 'sits_tae.R' 'sits_tempcnn.R' 'sits_terra.R' 'sits_texture.R' 'sits_timeline.R' 'sits_train.R' 'sits_tuning.R' 'sits_utils.R' 'sits_uncertainty.R' 'sits_validate.R' 'sits_view.R' 'sits_variance.R' 'sits_xlsx.R' 'zzz.R' Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev make libicu-dev libjpeg-dev libpng-dev libuv1-dev libxml2-dev libssl-dev libproj-dev libsqlite3-dev libudunits2-dev zlib1g-dev Repository: https://ropensci.r-universe.dev Date/Publication: 2026-01-14 18:08:19 UTC RemoteUrl: https://github.com/e-sensing/sits RemoteRef: master RemoteSha: dd33dfc0d32aa3f8a0f12804841c7cb80ae6c082 NeedsCompilation: yes Packaged: 2026-06-13 06:31:39 UTC; root 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]