| sits | sits-package sits |
| 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 by linear interpolation | impute_linear |
| Plot time series and data cubes | 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 SOM samples evaluated | plot.som_clean_samples |
| 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 | sits_accuracy sits_accuracy.class_cube sits_accuracy.class_vector_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.default sits_as_sf.raster_cube sits_as_sf.sits sits_as_sf.vector_cube |
| Convert a data cube into a stars object | sits_as_stars |
| Convert a data cube into a Spatial Raster object from terra | sits_as_terra sits_as_terra.class_cube sits_as_terra.probs_cube sits_as_terra.raster_cube sits_as_terra.uncertainty_cube sits_as_terra.variance_cube |
| 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.tbl_df |
| Classify a regular raster cube | sits_classify.raster_cube |
| Classify a segmented data cube | sits_classify.segs_cube sits_classify.vector_cube |
| Classify a set of time series | sits_classify.sits |
| 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 |
| 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 |
| Create a user configuration file. | sits_config_user_file |
| Create data cubes from image collections | sits_cube |
| Copy the images of a cube to a local directory | sits_cube_copy |
| Create sits cubes from cubes in flat files in a local | sits_cube.local_cube |
| Create a results cube from local files | sits_cube.results_cube |
| Create data cubes from image collections accessible by STAC | sits_cube.stac_cube |
| Create a vector cube from local files | sits_cube.vector_cube |
| 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 values from classified maps | sits_get_class sits_get_class.csv sits_get_class.data.frame sits_get_class.default sits_get_class.sf sits_get_class.shp sits_get_class.sits |
| Get time series from data cubes and cloud services | sits_get_data sits_get_data.default |
| Get time series using CSV files | sits_get_data.csv |
| Get time series using sits objects | sits_get_data.data.frame |
| Get time series using sf objects | sits_get_data.sf |
| Get time series using shapefiles | sits_get_data.shp |
| Get time series using sits objects | sits_get_data.sits |
| Get values from probability maps | sits_get_probs sits_get_probs.csv sits_get_probs.data.frame sits_get_probs.default sits_get_probs.sf sits_get_probs.shp sits_get_probs.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 |
| Change the labels of a set of time series | sits_labels<- |
| Change the labels of a set of time series | sits_labels<-.class_cube |
| Change the labels of a set of time series | sits_labels<-.default |
| Change the labels of a set of time series | sits_labels<-.probs_cube |
| Change the labels of a set of time series | sits_labels<-.sits |
| Train light gradient boosting model | sits_lightgbm |
| Train a model using Lightweight Temporal Self-Attention Encoder | sits_lighttae |
| List the cloud collections supported by sits | sits_list_collections |
| Train a Long Short Term Memory Fully Convolutional Network | sits_lstm_fcn |
| Merge two data sets (time series or cubes) | sits_merge sits_merge.default sits_merge.raster_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_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.combined_cube sits_regularize.default sits_regularize.dem_cube sits_regularize.derived_cube sits_regularize.ogh_cube sits_regularize.rainfall_cube sits_regularize.raster_cube sits_regularize.sar_cube |
| Train ResNet classification models | sits_resnet |
| Train random forest models | sits_rfor |
| Given a ROI, find MGRS tiles intersecting it. | sits_roi_to_mgrs |
| Find tiles of a given ROI and Grid System | sits_roi_to_tiles |
| 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 a data set (tibble or cube) for bands, tiles, and dates | sits_select sits_select.default 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.probs_vector_cube sits_smooth.raster_cube |
| Cleans the samples based on SOM map information | sits_som_clean_samples |
| Evaluate cluster | sits_som_evaluate_cluster |
| Build a SOM for quality analysis of time series samples | sits_som_map |
| 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 |
| Apply a set of texture measures on a data cube. | sits_texture sits_texture.default sits_texture.derived_cube sits_texture.raster_cube |
| Convert MGRS tile information to ROI in WGS84 | sits_tiles_to_roi |
| 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 a full sits tibble to the CSV format | sits_timeseries_to_csv |
| 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 sits_uncertainty.raster_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.class_vector_cube sits_view.data.frame sits_view.default sits_view.probs_cube sits_view.raster_cube sits_view.sits sits_view.som_map sits_view.uncertainty_cube sits_view.vector_cube |
| 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 |
| Summarize variance cubes | summary.variance_cube |
