This function classifies a set of time series or data cube given a set of training samples, an inference model, and an interval. To perform the classification, users should provide a set of labelled samples. Each samples should be associated to one spatial location (latitude/longitude), one time interval and a label. This is a generic function. The following specific functions are available:
"sits tibble": see sits_classify.sits
"cube": see sits_classify.raster_cube
SITS supports the following models:
support vector machines: see sits_svm
random forests: see sits_rfor
linear discriminant analysis: see sits_lda
quadratic discriminant analysis: see sits_qda
multinomial logit: see sits_mlr
extreme gradient boosting: see sits_xgboost
multi-layer perceptrons: see sits_deeplearning
1D convolutional neural networks: see sits_FCN
mixed 1D and MLP networks: see sits_TempCNN
1D version of ResNet: see sits_ResNet
combined LSTM-FCN model: see sits_LSTM_FCN
The model should be precomputed using sits_train
and then passed to the "sits_classify" function using parameter "ml_model".
sits_classify(data, ml_model, ...)Tibble with time series metadata and data.
Pre-built machine learning model
(see sits_train).
Other parameters to be passed to specific functions
Predicted data