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Python 3.10 Pytorch 1.12.1 License MIT

Focal_TSMP

This is the code to reproduce the results presented in the paper:

"Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation" by Mohamad Hakam Shams Eddin, and Juergen Gall. Published in Geoscientific Model Development.

Computer Vision Group, Institue of Computer Science, University of Bonn.

Project Page | Paper

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Model architecture

Example a

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Setup

For conda, you can install dependencies using yml file:

  conda env create -f Focal_TSMP.yml

Code

The code has been tested under Pytorch 1.12.1 and Python 3.10.6 on Ubuntu 20.04.5 LTS with NVIDIA RTX A6000 GPUs and NVIDIA GeForce RTX 3090 GPU.

The configuration file:

  config.py

The dataloader for TSMP dataset:

  TerrSysMP_NET_dataset.py

For training:

  train.py

For testing:

  test.py
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Dataset

To train on TSMP dataset:

To train on another dataset, you need to create a new dataloader class like TerrSysMP_NET_dataset.py

Checkpoints

Pretrained models can be downloaded from https://zenodo.org/doi/10.5281/zenodo.10015048

Citation

If you find our work useful in your research, please cite:


@Article{gmd-17-2987-2024,
AUTHOR = {Shams Eddin, M. H. and Gall, J.},
TITLE = {Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation},
JOURNAL = {Geoscientific Model Development},
VOLUME = {17},
YEAR = {2024},
NUMBER = {7},
PAGES = {2987--3023},
URL = {https://gmd.copernicus.org/articles/17/2987/2024/},
DOI = {10.5194/gmd-17-2987-2024}
}

Acknowledgments

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Collaborative Research Centre SFB 1502/1–2022 - DETECT - D05 and under Germany’s Excellence Strategy – EXC 2070– project no. 390732324.

License

The code is released under MIT License. See the LICENSE file for details.