A public repository of our work in missing views for Earth Observation (EO) applications.
Preprocessed data can be accessed at: Link
We use the following source for multi-view learning: https://github.com/fmenat/mvlearning
- To train a single-view learning model (e.g. Input-level fusion):
python train_singleview.py -s config/input.yaml
- To train all the views individually with single-view learning (e.g. for single-view predictions or Ensemble-based fusion):
python train_singleview_pool.py -s config/pool.yaml
- To train a multi-view learning model (e.g. Feature-level fusion, Decision-level fusion, Gated Fusion, Feature-level fusion with MultiLoss):
python train_multiview.py -s config/mv_feat.yaml
- To train a multi-view learning model with CCA searching in case of missing views:
python train_multiview_cca.py -s config/mv_cca.yaml
- To evaluate the model by its predictive quality:
python evaluate_predictions.py -s config/evaluation.yaml
- To evaluate the model by its predictive robustness:
python evaluate_rob_pred.py -s config/evaluation.yaml
Public repository of our IGARSS 2023 paper.
- ๐ Arxiv
Mena, Francisco, et al. "Impact assessment of missing data in model predictions for Earth observation applications." IEEE International Geoscience and Remote Sensing Symposiums (IGARSS), 2024.
@inproceedings{mena2024igarss,
title = {Impact assessment of missing data in model predictions for {Earth} observation applications},
booktitle = {{IEEE International Geoscience} and {Remote Sensing Symposium} ({IGARSS})},
author = {Mena, Francisco and Arenas, Diego and Charfuelan, Marcela and Nuske, Marlon and Dengel, Andreas},
year = {2024},
publisher = {{IEEE}},
}