/multi-stage-convSTAR-network

[RSE 2021] Crop mapping from image time series: deep learning with multi-scale label hierarchies

Primary LanguagePython

ms-convSTAR

Pytorch implementation for hierarchical time series classification with multi-stage convolutional RNN described in:

Crop mapping from image time series: deep learning with multi-scale label hierarchies. Turkoglu, Mehmet Ozgur and D'Aronco, Stefano and Perich, Gregor and Liebisch, Frank and Streit, Constantin and Schindler, Konrad and Wegner, Jan Dirk. Remote Sensing of Environment, 2021.

[Paper] - [Poster]

If you find our work useful in your research, please consider citing our paper:

@article{turkoglu2021msconvstar,
  title={Crop mapping from image time series: deep learning with multi-scale label hierarchies},
  author={Turkoglu, Mehmet Ozgur and D'Aronco, Stefano and Perich, Gregor and Liebisch, Frank and Streit, Constantin and Schindler, Konrad and Wegner, Jan Dirk},
  journal={Remote Sensing of Environment},
  volume={264},
  year={2021},
  publisher={Elsevier}
}

ZueriCrop Dataset

Download the dataset via https://polybox.ethz.ch/index.php/s/uXfdr2AcXE3QNB6

Getting Started

Train the model e.g., for fold:1 with

python3 train.py --data /path/to/data --fold 1

Test the trained model e.g., for fold:1 with

python3 test.py --data /path/to/data --fold 1 --snapshot /path/to/trained_model