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 |
sits |
sits |
sits_accuracy |
Assess classification accuracy (area-weighted method) |
sits_accuracy.class_cube |
Assess classification accuracy (area-weighted method) |
sits_accuracy.default |
Assess classification accuracy (area-weighted method) |
sits_accuracy.derived_cube |
Assess classification accuracy (area-weighted method) |
sits_accuracy.raster_cube |
Assess classification accuracy (area-weighted method) |
sits_accuracy.sits |
Assess classification accuracy (area-weighted method) |
sits_accuracy.tbl_df |
Assess classification accuracy (area-weighted method) |
sits_apply |
Apply a function on a set of time series |
sits_apply.default |
Apply a function on a set of time series |
sits_apply.derived_cube |
Apply a function on a set of time series |
sits_apply.raster_cube |
Apply a function on a set of time series |
sits_apply.sits |
Apply a function on a set of time series |
sits_as_sf |
Return a sits_tibble or raster_cube as an sf object. |
sits_as_sf.raster_cube |
Return a sits_tibble or raster_cube as an sf object. |
sits_as_sf.sits |
Return a sits_tibble or raster_cube as an sf object. |
sits_bands |
Get the names of the bands |
sits_bands.default |
Get the names of the bands |
sits_bands.patterns |
Get the names of the bands |
sits_bands.raster_cube |
Get the names of the bands |
sits_bands.sits |
Get the names of the bands |
sits_bands.sits_model |
Get the names of the bands |
sits_bands<- |
Get the names of the bands |
sits_bands<-.default |
Get the names of the bands |
sits_bands<-.raster_cube |
Get the names of the bands |
sits_bands<-.sits |
Get the names of the bands |
sits_bbox |
Get the bounding box of the data |
sits_bbox.default |
Get the bounding box of the data |
sits_bbox.raster_cube |
Get the bounding box of the data |
sits_bbox.sits |
Get the bounding box of the data |
sits_bbox.tbl_df |
Get the bounding box of the data |
sits_classify |
Classify time series or data cubes |
sits_classify.default |
Classify time series or data cubes |
sits_classify.derived_cube |
Classify time series or data cubes |
sits_classify.raster_cube |
Classify time series or data cubes |
sits_classify.segs_cube |
Classify time series or data cubes |
sits_classify.sits |
Classify time series or data cubes |
sits_classify.tbl_df |
Classify time series or data cubes |
sits_clean |
Cleans a classified map using a local window |
sits_clean.class_cube |
Cleans a classified map using a local window |
sits_clean.default |
Cleans a classified map using a local window |
sits_clean.derived_cube |
Cleans a classified map using a local window |
sits_clean.raster_cube |
Cleans a classified map using a local window |
sits_cluster_clean |
Removes labels that are minority in each cluster. |
sits_cluster_dendro |
Find clusters in time series samples |
sits_cluster_dendro.default |
Find clusters in time series samples |
sits_cluster_dendro.sits |
Find clusters in time series samples |
sits_cluster_frequency |
Show label frequency in each cluster produced by dendrogram analysis |
sits_colors |
Function to retrieve sits color table |
sits_colors_qgis |
Function to save color table as QML style for data cube |
sits_colors_reset |
Function to reset sits color table |
sits_colors_set |
Function to set sits color table |
sits_colors_show |
Function to show colors in SITS |
sits_combine_predictions |
Estimate ensemble prediction based on list of probs cubes |
sits_combine_predictions.average |
Estimate ensemble prediction based on list of probs cubes |
sits_combine_predictions.default |
Estimate ensemble prediction based on list of probs cubes |
sits_combine_predictions.uncertainty |
Estimate ensemble prediction based on list of probs cubes |
sits_confidence_sampling |
Suggest high confidence samples to increase the training set. |
sits_config |
Configure parameters for sits package |
sits_config_show |
Show current sits configuration |
sits_cube |
Create data cubes from image collections |
sits_cube.local_cube |
Create data cubes from image collections |
sits_cube.sar_cube |
Create data cubes from image collections |
sits_cube.stac_cube |
Create data cubes from image collections |
sits_cube_copy |
Copy the images of a cube to a local directory |
sits_factory_function |
Create a closure for calling functions with and without data |
sits_filter |
Filter time series with smoothing filter |
sits_formula_linear |
Define a linear formula for classification models |
sits_formula_logref |
Define a loglinear formula for classification models |
sits_geo_dist |
Compute the minimum distances among samples and prediction points. |
sits_get_data |
Get time series from data cubes and cloud services |
sits_get_data.csv |
Get time series from data cubes and cloud services |
sits_get_data.data.frame |
Get time series from data cubes and cloud services |
sits_get_data.default |
Get time series from data cubes and cloud services |
sits_get_data.sf |
Get time series from data cubes and cloud services |
sits_get_data.shp |
Get time series from data cubes and cloud services |
sits_get_data.sits |
Get time series from data cubes and cloud services |
sits_impute |
Replace NA values in time series with imputation function |
sits_kfold_validate |
Cross-validate time series samples |
sits_labels |
Get labels associated to a data set |
sits_labels.default |
Get labels associated to a data set |
sits_labels.derived_cube |
Get labels associated to a data set |
sits_labels.derived_vector_cube |
Get labels associated to a data set |
sits_labels.patterns |
Get labels associated to a data set |
sits_labels.raster_cube |
Get labels associated to a data set |
sits_labels.sits |
Get labels associated to a data set |
sits_labels.sits_model |
Get labels associated to a data set |
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 |
Change the labels of a set of time series |
sits_labels_summary |
Inform label distribution of a set of time series |
sits_labels_summary.sits |
Inform label distribution of a set of time series |
sits_label_classification |
Build a labelled image from a probability cube |
sits_label_classification.default |
Build a labelled image from a probability cube |
sits_label_classification.derived_cube |
Build a labelled image from a probability cube |
sits_label_classification.probs_cube |
Build a labelled image from a probability cube |
sits_label_classification.probs_vector_cube |
Build a labelled image from a probability cube |
sits_label_classification.raster_cube |
Build a labelled image from a probability cube |
sits_lighttae |
Train a model using Lightweight Temporal Self-Attention Encoder |
sits_list_collections |
List the cloud collections supported by sits |
sits_merge |
Merge two data sets (time series or cubes) |
sits_merge.default |
Merge two data sets (time series or cubes) |
sits_merge.raster_cube |
Merge two data sets (time series or cubes) |
sits_merge.sar_cube |
Merge two data sets (time series or cubes) |
sits_merge.sits |
Merge two data sets (time series or cubes) |
sits_mgrs_to_roi |
Convert MGRS tile information to ROI in WGS84 |
sits_mixture_model |
Multiple endmember spectral mixture analysis |
sits_mixture_model.default |
Multiple endmember spectral mixture analysis |
sits_mixture_model.derived_cube |
Multiple endmember spectral mixture analysis |
sits_mixture_model.raster_cube |
Multiple endmember spectral mixture analysis |
sits_mixture_model.sits |
Multiple endmember spectral mixture analysis |
sits_mixture_model.tbl_df |
Multiple endmember spectral mixture analysis |
sits_mlp |
Train multi-layer perceptron models using torch |
sits_model_export |
Export classification models |
sits_model_export.sits_model |
Export classification models |
sits_mosaic |
Mosaic classified cubes |
sits_patterns |
Find temporal patterns associated to a set of time series |
sits_predictors |
Obtain predictors for time series samples |
sits_pred_features |
Obtain numerical values of predictors for time series samples |
sits_pred_normalize |
Normalize predictor values |
sits_pred_reference |
Obtain categorical id and predictor labels for time series samples |
sits_pred_references |
Obtain categorical id and predictor labels for time series samples |
sits_pred_sample |
Obtain a fraction of the predictors data frame |
sits_reclassify |
Reclassify a classified cube |
sits_reclassify.class_cube |
Reclassify a classified cube |
sits_reclassify.default |
Reclassify a classified cube |
sits_reduce |
Reduces a cube or samples from a summarization function |
sits_reduce.raster_cube |
Reduces a cube or samples from a summarization function |
sits_reduce.sits |
Reduces a cube or samples from a summarization function |
sits_reduce_imbalance |
Reduce imbalance in a set of samples |
sits_regularize |
Build a regular data cube from an irregular one |
sits_regularize.default |
Build a regular data cube from an irregular one |
sits_regularize.derived_cube |
Build a regular data cube from an irregular one |
sits_regularize.raster_cube |
Build a regular data cube from an irregular one |
sits_regularize.sar_cube |
Build a regular data cube from an irregular one |
sits_resnet |
Train ResNet classification models |
sits_rfor |
Train random forest models |
sits_run_examples |
Informs if sits examples should run |
sits_run_tests |
Informs if sits tests should run |
sits_sample |
Sample a percentage of a time series |
sits_sampling_design |
Allocation of sample size to strata |
sits_segment |
Segment an image |
sits_select |
Filter bands on a data set (tibble or cube) |
sits_select.default |
Filter bands on a data set (tibble or cube) |
sits_select.patterns |
Filter bands on a data set (tibble or cube) |
sits_select.raster_cube |
Filter bands on a data set (tibble or cube) |
sits_select.sits |
Filter bands on a data set (tibble or cube) |
sits_sgolay |
Filter time series with Savitzky-Golay filter |
sits_slic |
Segment an image using SLIC |
sits_smooth |
Smooth probability cubes with spatial predictors |
sits_smooth.default |
Smooth probability cubes with spatial predictors |
sits_smooth.derived_cube |
Smooth probability cubes with spatial predictors |
sits_smooth.probs_cube |
Smooth probability cubes with spatial predictors |
sits_smooth.raster_cube |
Smooth probability cubes with spatial predictors |
sits_som |
Use SOM for quality analysis of time series samples |
sits_som_clean_samples |
Cleans the samples based on SOM map information |
sits_som_evaluate_cluster |
Evaluate cluster |
sits_som_map |
Use SOM for quality analysis of time series samples |
sits_stats |
Obtain statistics for all sample bands |
sits_stratified_sampling |
Allocation of sample size to strata |
sits_svm |
Train support vector machine models |
sits_tae |
Train a model using Temporal Self-Attention Encoder |
sits_tempcnn |
Train temporal convolutional neural network models |
sits_timeline |
Get timeline of a cube or a set of time series |
sits_timeline.default |
Get timeline of a cube or a set of time series |
sits_timeline.derived_cube |
Get timeline of a cube or a set of time series |
sits_timeline.raster_cube |
Get timeline of a cube or a set of time series |
sits_timeline.sits |
Get timeline of a cube or a set of time series |
sits_timeline.sits_model |
Get timeline of a cube or a set of time series |
sits_timeline.tbl_df |
Get timeline of a cube or a set of time series |
sits_to_csv |
Export a sits tibble metadata to the CSV format |
sits_to_csv.default |
Export a sits tibble metadata to the CSV format |
sits_to_csv.sits |
Export a sits tibble metadata to the CSV format |
sits_to_csv.tbl_df |
Export a sits tibble metadata to the CSV format |
sits_to_xlsx |
Save accuracy assessments as Excel files |
sits_to_xlsx.list |
Save accuracy assessments as Excel files |
sits_to_xlsx.sits_accuracy |
Save accuracy assessments as Excel files |
sits_train |
Train classification models |
sits_tuning |
Tuning machine learning models hyper-parameters |
sits_tuning_hparams |
Tuning machine learning models hyper-parameters |
sits_uncertainty |
Estimate classification uncertainty based on probs cube |
sits_uncertainty.default |
Estimate classification uncertainty based on probs cube |
sits_uncertainty.probs_cube |
Estimate classification uncertainty based on probs cube |
sits_uncertainty.probs_vector_cube |
Estimate classification uncertainty based on probs cube |
sits_uncertainty_sampling |
Suggest samples for enhancing classification accuracy |
sits_validate |
Validate time series samples |
sits_variance |
Calculate the variance of a probability cube |
sits_variance.default |
Calculate the variance of a probability cube |
sits_variance.derived_cube |
Calculate the variance of a probability cube |
sits_variance.probs_cube |
Calculate the variance of a probability cube |
sits_variance.raster_cube |
Calculate the variance of a probability cube |
sits_view |
View data cubes and samples in leaflet |
sits_view.class_cube |
View data cubes and samples in leaflet |
sits_view.data.frame |
View data cubes and samples in leaflet |
sits_view.default |
View data cubes and samples in leaflet |
sits_view.probs_cube |
View data cubes and samples in leaflet |
sits_view.raster_cube |
View data cubes and samples in leaflet |
sits_view.sits |
View data cubes and samples in leaflet |
sits_view.som_map |
View data cubes and samples in leaflet |
sits_view.uncertainty_cube |
View data cubes and samples in leaflet |
sits_view.vector_cube |
View data cubes and samples in leaflet |
sits_whittaker |
Filter time series with whittaker filter |
sits_xgboost |
Train extreme gradient boosting models |
summary.class_cube |
Summarize data cubes |
summary.raster_cube |
Summarize data cubes |
summary.sits |
Summarize sits |
summary.sits_accuracy |
Summarize accuracy matrix for training data |
summary.sits_area_accuracy |
Summarize accuracy matrix for area data |