LOAN

"Location-aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger Forecasting" by Mohamad Hakam Shams Eddin, Ribana Roscher and Juergen Gall. Published in IEEE Transactions on Geoscience and Remote Sensing

IEEE TGRS | Arxiv

Setup

For conda, you can install dependencies using yml file:

  conda env create -f environment.yml

or using requirements.txt:

  conda create --name LOAN --file requirements.txt

For pip:

  pip install -r requirements.txt

Code

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

The dataloader for FireCube dataset:

  FireCube_dataloader.py

For training:

  train.py

For testing:

  test.py

Dataset

To train on FireCube dataset, You can download the training/testing samples from https://zenodo.org/record/6528394 (~250GB).

Compress the zip file of the datasets.tar.gz and copy the file mean_std_train.json into the directory datasets/datasets_grl/npy/spatiotemporal

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

Checkpoints

Pretrained models can be downloaded from pretrained_models

Citation

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

@ARTICLE{LOAN,
  author={Shams Eddin, Mohamad Hakam and Roscher, Ribana and Gall, Juergen},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Location-Aware Adaptive Normalization: A Deep Learning Approach for Wildfire Danger Forecasting}, 
  year={2023},
  volume={61},
  number={},
  pages={1-18},
  doi={10.1109/TGRS.2023.3285401}}

  
@article{LOAN,
  title={Location-aware Adaptive Denormalization: A Deep Learning Approach For Wildfire Danger Forecasting},
  author={Mohamad Hakam Shams Eddin and Ribana Roscher and Juergen Gall},
  journal={ArXiv},
  year={2022},
  volume={abs/2212.08208}}

Acknowledgments

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the Collaborative Research Centre SFB 1502/1–2022 - DETECT - D05.

License

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