Package: sits 1.5.1
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, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. 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 as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference, and methods for active learning and uncertainty assessment. Supports object-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
Authors:
sits_1.5.1.tar.gz
sits_1.5.1.tar.gz(r-4.5-noble)sits_1.5.1.tar.gz(r-4.4-noble)
sits_1.5.1.tgz(r-4.4-emscripten)sits_1.5.1.tgz(r-4.3-emscripten)
sits.pdf |sits.html✨
sits/json (API)
NEWS
# Install 'sits' in R: |
install.packages('sits', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/e-sensing/sits/issues
- cerrado_2classes - Samples of classes Cerrado and Pasture
- point_mt_6bands - A time series sample with data from 2000 to 2016
- samples_l8_rondonia_2bands - Samples of Amazon tropical forest biome for deforestation analysis
- samples_modis_ndvi - Samples of nine classes for the state of Mato Grosso
Last updated 4 months agofrom:6f30a4b3a7. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 18 2024 |
R-4.5-linux-x86_64 | OK | Nov 18 2024 |
Exports:impute_linearsits_accuracysits_accuracy_summarysits_add_base_cubesits_applysits_as_sfsits_bandssits_bands<-sits_bboxsits_classifysits_cleansits_cluster_cleansits_cluster_dendrosits_cluster_frequencysits_colorssits_colors_qgissits_colors_resetsits_colors_setsits_colors_showsits_combine_predictionssits_confidence_samplingsits_configsits_config_showsits_config_user_filesits_cubesits_cube_copysits_factory_functionsits_filtersits_formula_linearsits_formula_logrefsits_geo_distsits_get_datasits_imputesits_kfold_validatesits_label_classificationsits_labelssits_labels_summarysits_labels<-sits_lighttaesits_list_collectionssits_mergesits_mgrs_to_roisits_mixture_modelsits_mlpsits_model_exportsits_mosaicsits_patternssits_pred_featuressits_pred_normalizesits_pred_referencessits_pred_samplesits_predictorssits_reclassifysits_reducesits_reduce_imbalancesits_regularizesits_rforsits_run_examplessits_run_testssits_samplesits_sampling_designsits_segmentsits_selectsits_sgolaysits_show_predictionsits_slicsits_smoothsits_som_clean_samplessits_som_evaluate_clustersits_som_mapsits_som_remove_samplessits_statssits_stratified_samplingsits_svmsits_taesits_tempcnnsits_timelinesits_to_csvsits_to_xlsxsits_trainsits_tuningsits_tuning_hparamssits_uncertaintysits_uncertainty_samplingsits_validatesits_variancesits_viewsits_whittakersits_xgboost
Dependencies:askpassbitbit64callrclassclassIntclicorocpp11crayoncurlDBIdescdplyre1071ellipsisfansigdalUtilitiesgenericsgluehttrjpegjsonliteKernSmoothlifecyclelubridatemagrittrMASSmimeopensslpillarpkgconfigpngprocessxproxypspurrrR6RcppRcppArmadillorlangrstacs2safetensorssfshowtextshowtextdbsliderstringistringrsyssysfontsterratibbletidyrtidyselecttimechangetorchunitsutf8vctrswarpwithrwkyaml
Readme and manuals
Help Manual
Help page | Topics |
---|---|
sits | sits-package sits |
Change the labels of a set of time series | sits_labels<- sits_labels<-.class_cube sits_labels<-.default sits_labels<-.probs_cube sits_labels<-.sits `sits_labels<-` |
Samples of classes Cerrado and Pasture | cerrado_2classes |
histogram of prob cubes | hist.probs_cube |
histogram of data cubes | hist.raster_cube |
Histogram | hist.sits |
Histogram uncertainty cubes | hist.uncertainty_cube |
Replace NA values with linear interpolation | impute_linear |
Plot time series | plot plot.sits |
Plot classified images | plot.class_cube |
Plot Segments | plot.class_vector_cube |
Plot DEM cubes | plot.dem_cube |
Make a kernel density plot of samples distances. | plot.geo_distances |
Plot patterns that describe classes | plot.patterns |
Plot time series predictions | plot.predicted |
Plot probability cubes | plot.probs_cube |
Plot probability vector cubes | plot.probs_vector_cube |
Plot RGB data cubes | plot.raster_cube |
Plot Random Forest model | plot.rfor_model |
Plot SAR data cubes | plot.sar_cube |
Plot confusion matrix | plot.sits_accuracy |
Plot a dendrogram cluster | plot.sits_cluster |
Plot confusion between clusters | plot.som_evaluate_cluster |
Plot a SOM map | plot.som_map |
Plot Torch (deep learning) model | plot.torch_model |
Plot uncertainty cubes | plot.uncertainty_cube |
Plot uncertainty vector cubes | plot.uncertainty_vector_cube |
Plot variance cubes | plot.variance_cube |
Plot RGB vector data cubes | plot.vector_cube |
Plot XGB model | plot.xgb_model |
A time series sample with data from 2000 to 2016 | point_mt_6bands |
Samples of Amazon tropical forest biome for deforestation analysis | samples_l8_rondonia_2bands |
Samples of nine classes for the state of Mato Grosso | samples_modis_ndvi |
Assess classification accuracy (area-weighted method) | sits_accuracy sits_accuracy.class_cube sits_accuracy.default sits_accuracy.derived_cube sits_accuracy.raster_cube sits_accuracy.sits sits_accuracy.tbl_df |
Add base maps to a time series data cube | sits_add_base_cube |
Apply a function on a set of time series | sits_apply sits_apply.default sits_apply.derived_cube sits_apply.raster_cube sits_apply.sits |
Return a sits_tibble or raster_cube as an sf object. | sits_as_sf sits_as_sf.raster_cube sits_as_sf.sits |
Get the names of the bands | sits_bands sits_bands.default sits_bands.patterns sits_bands.raster_cube sits_bands.sits sits_bands.sits_model sits_bands<- sits_bands<-.default sits_bands<-.raster_cube sits_bands<-.sits |
Get the bounding box of the data | sits_bbox sits_bbox.default sits_bbox.raster_cube sits_bbox.sits sits_bbox.tbl_df |
Classify time series or data cubes | sits_classify sits_classify.default sits_classify.derived_cube sits_classify.raster_cube sits_classify.segs_cube sits_classify.sits sits_classify.tbl_df |
Cleans a classified map using a local window | sits_clean sits_clean.class_cube sits_clean.default sits_clean.derived_cube sits_clean.raster_cube |
Removes labels that are minority in each cluster. | sits_cluster_clean |
Find clusters in time series samples | sits_cluster_dendro sits_cluster_dendro.default sits_cluster_dendro.sits |
Show label frequency in each cluster produced by dendrogram analysis | sits_cluster_frequency |
Function to retrieve sits color table | sits_colors |
Function to save color table as QML style for data cube | sits_colors_qgis |
Function to reset sits color table | sits_colors_reset |
Function to set sits color table | sits_colors_set |
Function to show colors in SITS | sits_colors_show |
Estimate ensemble prediction based on list of probs cubes | sits_combine_predictions sits_combine_predictions.average sits_combine_predictions.default sits_combine_predictions.uncertainty |
Suggest high confidence samples to increase the training set. | sits_confidence_sampling |
Configure parameters for sits package | sits_config |
Show current sits configuration | sits_config_show |
List the cloud collections supported by sits | sits_config_user_file |
Create data cubes from image collections | sits_cube sits_cube.local_cube sits_cube.sar_cube sits_cube.stac_cube |
Copy the images of a cube to a local directory | sits_cube_copy |
Create a closure for calling functions with and without data | sits_factory_function |
Filter time series with smoothing filter | sits_filter |
Define a linear formula for classification models | sits_formula_linear |
Define a loglinear formula for classification models | sits_formula_logref |
Compute the minimum distances among samples and prediction points. | sits_geo_dist |
Get time series from data cubes and cloud services | sits_get_data sits_get_data.csv sits_get_data.data.frame sits_get_data.default sits_get_data.sf sits_get_data.shp sits_get_data.sits |
Replace NA values in time series with imputation function | sits_impute |
Cross-validate time series samples | sits_kfold_validate |
Build a labelled image from a probability cube | sits_label_classification sits_label_classification.default sits_label_classification.derived_cube sits_label_classification.probs_cube sits_label_classification.probs_vector_cube sits_label_classification.raster_cube |
Get labels associated to a data set | sits_labels sits_labels.default sits_labels.derived_cube sits_labels.derived_vector_cube sits_labels.patterns sits_labels.raster_cube sits_labels.sits sits_labels.sits_model |
Inform label distribution of a set of time series | sits_labels_summary sits_labels_summary.sits |
Train a model using Lightweight Temporal Self-Attention Encoder | sits_lighttae |
List the cloud collections supported by sits | sits_list_collections |
Merge two data sets (time series or cubes) | sits_merge sits_merge.default sits_merge.raster_cube sits_merge.sar_cube sits_merge.sits |
Convert MGRS tile information to ROI in WGS84 | sits_mgrs_to_roi |
Multiple endmember spectral mixture analysis | sits_mixture_model sits_mixture_model.default sits_mixture_model.derived_cube sits_mixture_model.raster_cube sits_mixture_model.sits sits_mixture_model.tbl_df |
Train multi-layer perceptron models using torch | sits_mlp |
Export classification models | sits_model_export sits_model_export.sits_model |
Mosaic classified cubes | sits_mosaic |
Find temporal patterns associated to a set of time series | sits_patterns |
Obtain numerical values of predictors for time series samples | sits_pred_features |
Normalize predictor values | sits_pred_normalize |
Obtain categorical id and predictor labels for time series samples | sits_pred_reference sits_pred_references |
Obtain a fraction of the predictors data frame | sits_pred_sample |
Obtain predictors for time series samples | sits_predictors |
Reclassify a classified cube | sits_reclassify sits_reclassify.class_cube sits_reclassify.default |
Reduces a cube or samples from a summarization function | sits_reduce sits_reduce.raster_cube sits_reduce.sits |
Reduce imbalance in a set of samples | sits_reduce_imbalance |
Build a regular data cube from an irregular one | sits_regularize sits_regularize.default sits_regularize.dem_cube sits_regularize.derived_cube sits_regularize.raster_cube sits_regularize.sar_cube |
Train random forest models | sits_rfor |
Informs if sits examples should run | sits_run_examples |
Informs if sits tests should run | sits_run_tests |
Sample a percentage of a time series | sits_sample |
Allocation of sample size to strata | sits_sampling_design |
Segment an image | sits_segment |
Filter bands on a data set (tibble or cube) | sits_select sits_select.default sits_select.patterns sits_select.raster_cube sits_select.sits |
Filter time series with Savitzky-Golay filter | sits_sgolay |
Segment an image using SLIC | sits_slic |
Smooth probability cubes with spatial predictors | sits_smooth sits_smooth.default sits_smooth.derived_cube sits_smooth.probs_cube sits_smooth.raster_cube |
Use SOM for quality analysis of time series samples | sits_som sits_som_map |
Cleans the samples based on SOM map information | sits_som_clean_samples |
Evaluate cluster | sits_som_evaluate_cluster |
Evaluate cluster | sits_som_remove_samples |
Obtain statistics for all sample bands | sits_stats |
Allocation of sample size to strata | sits_stratified_sampling |
Train support vector machine models | sits_svm |
Train a model using Temporal Self-Attention Encoder | sits_tae |
Train temporal convolutional neural network models | sits_tempcnn |
Get timeline of a cube or a set of time series | sits_timeline sits_timeline.default sits_timeline.derived_cube sits_timeline.raster_cube sits_timeline.sits sits_timeline.sits_model sits_timeline.tbl_df |
Export a sits tibble metadata to the CSV format | sits_to_csv sits_to_csv.default sits_to_csv.sits sits_to_csv.tbl_df |
Save accuracy assessments as Excel files | sits_to_xlsx sits_to_xlsx.list sits_to_xlsx.sits_accuracy |
Train classification models | sits_train |
Tuning machine learning models hyper-parameters | sits_tuning |
Tuning machine learning models hyper-parameters | sits_tuning_hparams |
Estimate classification uncertainty based on probs cube | sits_uncertainty sits_uncertainty.default sits_uncertainty.probs_cube sits_uncertainty.probs_vector_cube |
Suggest samples for enhancing classification accuracy | sits_uncertainty_sampling |
Validate time series samples | sits_validate |
Calculate the variance of a probability cube | sits_variance sits_variance.default sits_variance.derived_cube sits_variance.probs_cube sits_variance.raster_cube |
View data cubes and samples in leaflet | sits_view sits_view.class_cube sits_view.data.frame sits_view.default sits_view.probs_cube sits_view.raster_cube sits_view.sits sits_view.som_map |
Filter time series with whittaker filter | sits_whittaker |
Train extreme gradient boosting models | sits_xgboost |
Summarize data cubes | summary.class_cube |
Summarize data cubes | summary.raster_cube |
Summarize sits | summary.sits |
Summarize accuracy matrix for training data | summary.sits_accuracy |
Summarize accuracy matrix for area data | summary.sits_area_accuracy |
Summarise variance cubes | summary.variance_cube |