Package: sits
Type: Package
Version: 1.5.3
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('aut'), email = 'lorena.santos@inpe.br')
)
Maintainer: Gilberto Camara <gilberto.camara.inpe@gmail.com>
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.
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.
Encoding: UTF-8
Language: en-US
Depends: R (>= 4.1.0)
URL: https://github.com/e-sensing/sits/, https://e-sensing.github.io/sitsbook/
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,
leafgl,
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
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,
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: 7.3.2
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_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_preconditions.R'
'api_raster.R'
'api_raster_sub_image.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_deafrica.R'
'api_source_deaustralia.R'
'api_source_hls.R'
'api_source_local.R'
'api_source_mpc.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_histogram.R'
'sits_imputation.R'
'sits_labels.R'
'sits_label_classification.R'
'sits_lighttae.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_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'
(a) Vignettes: Instead of preparing vignettes, the authors have written an online book that describes the contents of the package in detail. The book is available at the URL https://e-sensing.github.io/sitsbook/
(1) To run the tests, examples, and code coverage, please make
Ensure that the following environment variables are set in the R session.
Sys.setenv("SITS_RUN_TESTS" = "YES")
Sys.setenv("SITS_RUN_EXAMPLES" = "YES")sits is a fairly large package, and the tests take a long time to run, since they access cloud services. We must manually enable testing for this reason.
(2) Please review version 1.5.3, not yet on CRAN, which is available in the "dev" branch in the GitHub repository.
Confirm each of the following by checking the box.
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