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code of igarss publication
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‎README.md‎

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# MultiviewCropClassification
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Code used for the crop classification (CropHarvest) based on multi-view data fusion
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### Training
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* To train a single-view learning model (e.g. Input-level fusion):
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```
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python train_singleview.py -s config/singleview_ex.yaml
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```
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* To train all the views individually with single-view learning (e.g. for single-view predictions or Ensemble-based fusion):
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```
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python train_singleview_pool.py -s config/singleviewpool_ex.yaml
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```
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* To train a multi-view learning model (e.g. Feature-level fusion, Decision-level fusion, Gated Fusion, Feature-level fusion with MultiLoss):
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```
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python train_multiview.py -s config/multiview_ex.yaml
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```
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### Evaluation
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* To evaluate the model by its predictions (performance):
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```
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python evaluate_predictions.py -s config/evaluation_ex.yaml
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```
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## Installation
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Please install the required packages with the following command:
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```
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pip install -r requirements.txt
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```
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> for torch
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```
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pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
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```
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## Data
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The data used comes from https://github.com/nasaharvest/cropharvest. However we also share the structures that we used on Google Drive: https://drive.google.com/drive/folders/1aPlctAL8B5dXSdpM55fr3-RUmAHO3quj
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## Source
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Public repository of our IGARSS 2023 submission.
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## Citation
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Not yet..
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## Licence
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Copyright (C) 2022 authors of this github.
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.

‎config/evaluation_ex.yaml‎

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output_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/outputs
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input_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/data
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data_name: cropharvest_Kenya_maize #options are cropharvest_Kenya_maize, cropharvest_global_crop, cropharvest_togo-eval_crop
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methods_to_plot: null #consider all
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plot_runs: False

‎config/multiview_ex.yaml‎

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output_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/outputs
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input_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/data
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data_name: cropharvest_togo-eval_crop
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training:
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max_epochs: 1000
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batch_size: 256
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emb_dim: 64
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early_stop_args:
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min_delta: 0.01
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mode: min
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patience: 5
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verbose: True
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experiment:
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runs: 10 #repetitions
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val_size: 0.1
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mlflow_runs_exp: True
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preprocess:
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fillnan: False
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flatten: False
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form: zscore
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save_individually: False #if representations are saved individually -- only for feature based
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view_names: ["S2","S1","weather", "VI", "DEM"]
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architecture:
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encoders:
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S1:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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S2:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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weather:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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VI:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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DEM:
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model_type: mlp
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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predictive_model:
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: True
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method: #fusion method
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feature: True
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agg_args:
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mode: avg
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adaptive: True
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features: True

‎config/multiview_multiloss_ex.yaml‎

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output_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/outputs
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input_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/data
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data_name: cropharvest_togo-eval_crop
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training:
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max_epochs: 1000
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batch_size: 256
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emb_dim: 64
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early_stop_args:
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min_delta: 0.01
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mode: min
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patience: 5
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verbose: True
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loss_args:
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multi: True
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weights_loss: 0.3
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experiment:
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runs: 10 #repetitions
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val_size: 0.1
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mlflow_runs_exp: True
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preprocess:
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fillnan: False
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flatten: False
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form: zscore
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save_individually: False #if representations are saved individually -- only for feature based
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view_names: ["S2","S1","weather", "VI", "DEM"]
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architecture:
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encoders:
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S1:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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S2:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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weather:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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VI:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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DEM:
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model_type: mlp
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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predictive_model:
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: True
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method: #fusion method
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feature: True
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agg_args:
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mode: avg
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adaptive: True
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features: True

‎config/singleview_ex.yaml‎

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output_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/outputs
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input_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/data
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data_name: cropharvest_togo-eval_crop
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training:
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max_epochs: 1000
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batch_size: 256
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emb_dim: 64
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early_stop_args:
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min_delta: 0.01
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mode: min
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patience: 5
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verbose: True
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experiment:
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runs: 10
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val_size: 0.1
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mlflow_runs_exp: True
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preprocess:
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fillnan: False
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flatten: False
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form: zscore
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view_names: ["S2","S1","weather", "VI"]
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architecture:
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encoders:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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predictive_model:
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: True

‎config/singleviewpool_ex.yaml‎

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output_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/outputs
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input_dir_folder: /home/mena/Documents/PhD/MultiviewCropClassification/data
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data_name: cropharvest_togo-eval_crop
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training:
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max_epochs: 1000
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batch_size: 256
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emb_dim: 64
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early_stop_args:
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min_delta: 0.01
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mode: min
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patience: 5
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verbose: True
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experiment:
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runs: 10
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val_size: 0.1
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mlflow_runs_exp: True
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preprocess:
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fillnan: False
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flatten: False
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form: zscore
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view_names: ["S2","S1","weather", "VI", "DEM"]
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architecture:
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encoders:
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S1:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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S2:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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weather:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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VI:
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model_type: gru
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n_layers: 2
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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DEM:
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model_type: mlp
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: False
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predictive_model:
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n_layers: 1
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layer_size: 64
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dropout: 0.2
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batchnorm: True

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