In the search for an optimal multi-view crop classifier

Public repository of our comparative work on various data fusion strategies and encoder architectures for crop classification.

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Training

• To train the Input fusion strategy:

python train_singleview.py -s config/singleview_ex.yaml

• To train the Ensemble aggregation strategy:

python train_singleview_pool.py -s config/singleviewpool_ex.yaml

• To train strategies based on multiple encoders: Feature, Decision, and Hybrid fusion strategies:

python train_multiview.py -s config/multiview_ex.yaml

Data

The data used comes from https://github.com/nasaharvest/cropharvest. However, we also share the code used to generate the data structures that we used in data folder.

Evaluation

• To evaluate the model by its predictions (performance):

python evaluate_predictions.py -s config/evaluation_ex.yaml

Installation

Please install the required packages with the following command:

pip install -r requirements.txt

🖊️ Citation

Mena, Francisco, et al. "In the search for optimal multi-view learning models for crop classification with global remote sensing data." International Journal of Applied Earth Observation and Geoinformation, 2025.

@article{mena2025optimal,
  title = {In the search for optimal multi-view learning models for crop classification with global remote sensing data},
  author = {Mena, Francisco and Arenas, Diego and Dengel, Andreas},
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  year = {2025},
  doi = {10.1016/j.jag.2025.104823},
  volume={143},
}