sits_roi_to_tiles functionsits_get_data() implementationsits_mosaic()sits_clean() multicores operationssits_view() using leafglsits_summary() and sits_stratified samplingsits_regularize()sits_select()exclusion_mask parameter in sits_classify() and sits_smooth()sits_regularize(), including MGRS and Brazil Data Cube gridssits_merge() implementation to better handle multiple scenario casesroi when plotting data cubessits_cube_copy() to improve timeout handling and efficiencysits_list_collections()SpatExtent object from terra as roi in sits_cube()crs usage in sits_get_data() to support WKTsits_classify() performance with segments classification.reg_cube_split_assets() for R 4.X compatibilitysits_merge() function that was not merging SAR and OPTICAL cubessits_view()plot() performance using raster overviewssits_cube()sits_mosaic()sits_segment() using chunk parallelizationsits_clean() function to improve classified mapssits_sampling_design() and sits_stratified_sampling()sits_reduce() functiondtw distance when building SOM mapssits_classify() segmentssits_apply()supercells packagesits_get_data() to extract average values of time series based on segmentssits_view()summary() function to show details of data cubes and time series tibblessits_mosaic() function for improving visualization of large data setssits_regularize()sits_cube_copy() for downloading data from the internetsitssits_train()sits_combine_predictions()data.table package.raster_file_blocksize.terra() bug (issue #918)stars proxy bug (issue #902)purrr cross deprecationggplot2 aes_string deprecationtibble subsetting bug (issue #893)sits_som_clean_samples() bug (issue #890)sits_get_data() can be used to retrieve samples in classified cubesits_mixture_model())sits_mosaic_cubes())sits_model())sits_cube_copy())sits_combine_predictions())sits_plot)sits_apply()sits_regularize() (issue #848)sits_labels()<- (issue #846)sits_label_classification() and sits_smooth() (issue #850)sits_classify() on BDC cubes (issue #844)sits_apply()sits_apply()sits_applysits_mixture_model for spectral mixture analysissits_viewsits_as_sf to convert sits objects to sfsits_regularizeroi parameter in sits_regularize functioncrs parameter in sits_get_data"MPC"sits_whittaker() function to process cube.sits_lighttae() (Lightweight Temporal Self-Attention)sits_uncertainty_sampling() for active learningsits_confidence_samples() for semi-supervised learningsits_geo_dist() to generate samples-samples and samples-predicted plotsits_tuning() for random search of machine learning parameterssits_reduce_imbalance() function to balance class samplessits_as_sf() to convert a sits tibble to a sf objecttorchopt deep learning optimizer packagesits_uncertainty(): least confidence and margin of confidencesits_kfold_validate()data to samples in sits machine learning classifiers (NOTE: models trained in previous versions is no longer supported)file parameter in sits_get_data() functiontorch package and remove keras dependencesits_TAE() classification modelsits_lightgbm() classification modelsits_regularize() parameterssits_regularize() to reach production level qualitysits_regularize() to use C++ internal functionssits_cube() to open results cubeplot() parameters on raster cubessits_view()sits_get_data() to accept tibblessits_cube()sits_regularize() to process in parallel by tiles, bands, and datessits_regularize() to check malformed filesAWS_NO_SIGN_REQUEST environment variable.gc_get_valid_interval() function.sits_regularize has a fault tolerance system, so that if there is a processing error the function will delete the malformed files and create them again.sits_regularize function has a new parameter called multithreads.sits_cube function for local cubes has a new parameter called multicores.F1 score in sits_kfold_validate with more than 2 labels.sits_cube() function to tolerate malformed paths from STAC service;sits_apply() function to generate new bands from existing ones;sits_accuracy() function to work with multiple cubes;sits_view()sits_uncertainty() function to provide uncertainty measure to probability maps;sits_regularize() by taking least cloud cover by default method to compose imagessits_regularize that generated images with artifactssits_cube from STAC AWS Sentinel-2sits_timeline() to sits model objectsconfig_colors.yml by removing palette namessits_regularize()start_date and end_date from validation csv filesits_regularize() is producing Float64 images as outputgdalcubes_chunk_size in âconfig.ymlâ to improve sits_regularize()..source_collection_access_test to pass ellipsis to rstac::post_request function..source_collection_access_test to pass ellipsis to rstac::post_request function.sits_plotsits_timeline for cubes that do not have the same temporal extent.S2_10_16D_STK-1 removed from BDC source in config fileNoClass label improvementmapview to leaflet packageCLASSIFIED and PROBS sources from config fileterra package to 1.4-11sits_list_collections() to indicate open data collectionptw, signal and MASSopen_data collections in config fileoutput_dir parametersits_cube_clone() functionsits_select() for bands in raster cubesits_regularize() functionOPENDATA sourceS2_10-1 BDC collection from configsits_list_collections().source_bands_resampling()sits_som_clean_samples() functionsits_bands<-() functionsits_select() functionsits_bbox() functionS2-SEN2COR_10_16D_STK-1 BDC collectioncheck functionsatellite and sensor info in config fileimager, ranger, proto, and future packages from sitssits_cube.local_cube() function parameters satellite and sensororigin and collection to sits_cube.local_cube() functionroi parameter in sits_classify() functionRaster classification results can now have versions: a new parameter âversionâ has been included in the sits_classify function.
Corrections to sits_kohonen and to the documentation.
New deep learning models for time series: 1D convolutional neural networks (sits_FCN), combining 1D CNN and multi-layer perceptron networks (sits_TempCNN), 1D version of ResNet (sits_ResNet), and combination of long-short term memory (LSTM) and 1D CNN (sits_LSTM_FCN).
New version of area accuracy measures that include Olofsson metrics ()
From version 0.8 onwards, the package has been designed to work with data cubes. All references to âcoverageâ have been replaced by references to âcubesâ.
The classification of raster images using sits_classify now produces images with the information on the probability of each class for each pixel. This allows more flexibility in the options for labeling the resulting probability raster files.
The function sits_label_classification has been introduced to generate a labelled image from the class probability files, with optional smoothing. The choices are smoothing = none (default), smoothing = bayesian (for bayesian smoothing) and smoothing = majority (for majority smoothing).
To better define a cube, the metadata tibble associated to a cube requires four parameters to define the cube: (a) the web service that provides time series or cubes; (b) the URL of the web service; (c) the name of the satellite; (d) the name of the satellite sensor. If not provided, these parameters are inferred for the sits configuration file.
The functions that do data transformations, such as sits_tasseled_cap and sits_savi now require a sensor parameter (âMODISâ is the default)
Functions sits_bands and sits_labels now work for both tibbles with time series and data cubes.
sits_show_config() to see the default contents. Users can override these parameters or add their own by creating a config.yml file in their home directory.Examples and demos that include classification of raster files now use the inSitu R package, available using devtools::install_github(e-sensing/inSitu).
All examples have been tested and checked for correctness.
sits_coverage has been replaced by sits_cube.
sits_raster_classification has been removed. Please use sits_classify.
In sits_classify, the parameter out_prefix has been changed to output_dir, to allow better control of the directory on which to write.
sits_bayes_smooth has been removed. Please use sits_label_classification with smoothing = bayesian.
To define a cube based on local files, service = RASTER has been replaced by service = LOCALHOST.
For programmers only: The sits_cube.R file now includes many convenience functions to avoid using cumbersome indexes to files and vector: .sits_raster_params, .sits_cube_all_robjs, .sits_class_band_name, .sits_cube_bands, .sits_cube_service, .sits_cube_file, .sits_cube_files, .sits_cube_labels, .sits_cube_timeline, .sits_cube_robj, .sits_cube_all_robjs, .sits_cube_missing_values, .sits_cube_minimum_values, .sits_cube_maximum_values, .sits_cube_scale_factors, .sits_files_robj. Please look at the documentation provided in the sits_cube.R file.
For programmers only: The metadata that describes the data cube no longer stores the raster objects associated to the files associated with the cube.
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