Satellite Image Time Series Analysis for Earth Observation Data Cubes
Check is date is valid
histogram of prob cubes
histogram of data cubes
Histogram
Histogram uncertainty cubes
Replace NA values by linear interpolation
Plot classified images
Plot Segments
Plot DEM cubes
Make a kernel density plot of samples distances.
Plot patterns that describe classes
Plot time series predictions
Plot probability cubes
Plot probability vector cubes
Plot RGB data cubes
Plot time series and data cubes
Plot Random Forest model
Plot SAR data cubes
Plot confusion matrix
Plot a dendrogram cluster
Plot SOM samples evaluated
Plot confusion between clusters
Plot a SOM map
Plot Torch (deep learning) model
Plot uncertainty cubes
Plot uncertainty vector cubes
Plot variance cubes
Plot RGB vector data cubes
Plot XGB model
Print the values of a confusion matrix
Print the area-weighted accuracy
Print accuracy summary
Assess classification accuracy
Add base maps to a time series data cube
Apply a function on a set of time series
Return a sits_tibble or raster_cube as an sf object.
Convert a data cube into a stars object
Convert a data cube into a Spatial Raster object from terra
Get the names of the bands
Get the bounding box of the data
Classify a regular raster cube
Classify time series or data cubes
Classify a segmented data cube
Classify a set of time series
Cleans a classified map using a local window
Removes labels that are minority in each cluster.
Find clusters in time series samples
Show label frequency in each cluster produced by dendrogram analysis
Function to save color table as QML style for data cube
Function to reset sits color table
Function to set sits color table
Function to show colors in SITS
Function to retrieve sits color table
Estimate ensemble prediction based on list of probs cubes
Suggest high confidence samples to increase the training set.
Show current sits configuration
Create a user configuration file.
Configure parameters for sits package
Copy the images of a cube to a local directory
Create sits cubes from cubes in flat files in a local
Create data cubes from image collections
Create a results cube from local files
Create data cubes from image collections accessible by STAC
Create a vector cube from local files
Create a closure for calling functions with and without data
Filter time series with smoothing filter
Define a linear formula for classification models
Define a loglinear formula for classification models
Compute the minimum distances among samples and prediction points.
Get values from classified maps
Get time series using CSV files
Get time series using sits objects
Get time series from data cubes and cloud services
Get time series using sf objects
Get time series using shapefiles
Get time series using sits objects
Get values from probability maps
Replace NA values in time series with imputation function
Cross-validate time series samples
Build a labelled image from a probability cube
Inform label distribution of a set of time series
Change the labels of a set of time series
Change the labels of a set of time series
Change the labels of a set of time series
Change the labels of a set of time series
Change the labels of a set of time series
Get labels associated to a data set
Train light gradient boosting model
Train a model using Lightweight Temporal Self-Attention Encoder
List the cloud collections supported by sits
Train a Long Short Term Memory Fully Convolutional Network
Merge two data sets (time series or cubes)
Convert MGRS tile information to ROI in WGS84
Multiple endmember spectral mixture analysis
Train multi-layer perceptron models using torch
Export classification models
Mosaic classified cubes
Find temporal patterns associated to a set of time series
Obtain numerical values of predictors for time series samples
Normalize predictor values
Obtain categorical id and predictor labels for time series samples
Obtain a fraction of the predictors data frame
Obtain predictors for time series samples
Reclassify a classified cube
Reduce imbalance in a set of samples
Reduces a cube or samples from a summarization function
Build a regular data cube from an irregular one
Train ResNet classification models
Train random forest models
Given a ROI, find MGRS tiles intersecting it.
Find tiles of a given ROI and Grid System
Informs if sits examples should run
Informs if sits tests should run
Sample a percentage of a time series
Allocation of sample size to strata
Segment an image
Filter a data set (tibble or cube) for bands, tiles, and dates
Filter time series with Savitzky-Golay filter
Shows the predicted labels for a classified tibble
Segment an image using SLIC
Smooth probability cubes with spatial predictors
Cleans the samples based on SOM map information
Evaluate cluster
Build a SOM for quality analysis of time series samples
Evaluate cluster
Obtain statistics for all sample bands
Allocation of sample size to strata
Train support vector machine models
Train a model using Temporal Self-Attention Encoder
Train temporal convolutional neural network models
Apply a set of texture measures on a data cube.
Convert MGRS tile information to ROI in WGS84
Get timeline of a cube or a set of time series
Export a a full sits tibble to the CSV format
Export a sits tibble metadata to the CSV format
Save accuracy assessments as Excel files
Train classification models
Tuning machine learning models hyper-parameters
Tuning machine learning models hyper-parameters
Suggest samples for enhancing classification accuracy
Estimate classification uncertainty based on probs cube
Validate time series samples
Calculate the variance of a probability cube
View data cubes and samples in leaflet
Filter time series with whittaker filter
Train extreme gradient boosting models
sits
Summarize data cubes
Summarize data cubes
Summarize accuracy matrix for training data
Summarize accuracy matrix for area data
Summarize sits
Summarize variance cubes
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. Enables merging of multi-source imagery (SAR, optical, DEM). 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. Includes object-based spatio-temporal segmentation for space-time OBIA. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
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