Pre-trained Models for SMP Classification and Segmentation

This dataset provides access to pre-trained models that were used for SnowMicroPen profile classification and segmentation. The models were trained on a part of the MOSAiC SMP dataset, available on https://doi.pangaea.de/10.1594/PANGAEA.935554. The labeled training data consists mostly of profiles from leg three of the expedition (January - May 2020), some profiles from leg one and two, and no profiles from leg four. Please refer to the snowdragon GitHub repository (https://github.com/liellnima/snowdragon) to access the models' training code and be directed to current publications.

The following trained models are available here (alphabetically ordered):

• Artificial neural networks
  • Bi-directional long short-term memory (blstm.hdf5)
  • Encoder-decoder (enc_dec.hdf5)
  • Long short-term memory (lstm.hdf5)
• Baseline
  • Majority vote classifier (baseline.model)
• Semi-supervised models
  • Cluster-then-predict models:
    • Bayesian Gaussian mixture model (gmm.model)
    • Bayesian mixture model (bmm.model)
    • K-means clustering (kmeans.model)
  • Label propagation (label_spreading.model)
  • Self-trained classifier (self_trainer.model)
• Supervised models
  • Balanced random forest (rf_bal.model)
  • Easy ensemble (easy_ensemble.model)
  • K-nearest neighbors (knn.model)
  • Random forest (rf.model)
  • Support vector machines (svm.model)

Loading Instructions:
The models with the file-ending ".model" are pickeled Python objects and can be loaded with ``pickle.load(your_model.model)``. The random forest must be loaded with ``joblib.load(rf.model)``. All artificial neural networks are h5py.File objects (tf.keras models) and can be loaded with ``tf.keras.models.load_model(your_ann.model)``.