Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean

🆕 2023-09: Accepted at Neurips 2023 Datasets and Benchmarks Track

This is the official code repository of the mesogeos dataset.

Pre-print describing the paper.

This repo contains code for the following: * Creation of the Mesogeos datacube. * Extraction of machine learning datasets for different tracks. * Training and evaluation of machine learning models for these tracks.

Authors: Spyros Kondylatos (1, 2), Ioannis Prapas (1, 2), Gustau Camps-Valls (2), Ioannis Papoutsis (1)

(1) Orion Lab, IAASARS, National Observatory of Athens

(2) Image & Signal Processing Group, Universitat de València

Table of Contents

• Downloading the data
• Datacube Generation
• Machine Learning Tracks
  • Track A: Wildfire Danger Forecasting
  • Track B: Final Burned Area Prediction
• Contributing
• Datacube Details
• Citation
• License
• Acknowledgements

Data repository

You can access the data using this Drive link. This link contains the mesogeos datacube (mesogeos_cube.zarr/), the extracted datasets for the machine learning tracks (ml_tracks/), as well as notebooks showing how to access the mesogeos cubes (notebooks/).

Accessing the mesogeos cube

The mesogeos cube is publicly accessible in the following places:

• Google Drive folder: https://drive.google.com/drive/folders/1aRXQXVvw6hz0eYgtJDoixjPQO-_bRK z9

• ~~S3 Storage in OVH~~ (This option isn't supported anymore due to supporting project termination)

Option 1: Access from Google Colab

notebooks/1ExploringMesogeos.ipynb shows how to open Mesogeos directly in google colab

Option 2: Download from Google Colab

Rclone may be the best option to download the dataset from google drive. See this issue.

Datacube Generation

Find the code to generate a datacube like mesogeos in datacube_creation.

Machine Learning Tracks

Track A: Wildfire Danger Forecasting

This track defines wildfire danger forecasting as a binary classification problem.

More details in Track A

Track B: Final Burned Area Prediction

This track is about predicting the final burned area of a wildfire given the ignition point and the conditions of the fire drivers at the first day of the fire in a neighborhood around the ignition point.

More details in Track B

Datacube Details

Mesogeos is meant to be used to develop models for wildfire modeling in the Mediterranean. It contains variables related to the ignition and spread of wildfire for the years 2006 to 2022 at a daily 1km x 1km grid.

An example of some variables for a day in the cube:

Datacube Metadata

• Temporal Extent: (2006-04-01, 2022-09-29)
• Spatial Extent: (-10.72, 30.07, 36.74, 47.7), i.e. the wider Mediterranean region.
• Coordinate Reference System: EPSG:4326

Datacube Citation

Spyros Kondylatos, Ioannis Prapas, Gustau Camps-Valls, & Ioannis Papoutsis. (2023). Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean. Zenodo. https://doi.org/10.5281/zenodo.7473331

Contributing

We welcome new contributions for new models and new machine learning tracks!

New Model: To contribute a new model for an existing track, your code has to be (i) open, (ii) reproducible (we should be able to easily run your code and get the reported results) and (iii) use the same dataset split defined for the track. After we verify your results, you get to add your model and name to the leaderboard. Check the current leaderboards.

Submit a new issue containing a link to your code.

New ML Track: To contribute a new track, submit a new issue.

We recommend at minimum:

1. a dataset extraction process that samples from mesogeos,
2. a description of the task,
3. a baseline model,
4. appropriate metrics.

License

Creative Commons Attribution v4

Citation

@inproceedings{ kondylatos2023mesogeos, title={Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean}, author={Spyros Kondylatos and Ioannis Prapas and Gustau Camps-Valls and Ioannis Papoutsis}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023}, url={https://openreview.net/forum?id=VH1vxapUTs} }

Acknowledgements

This work has received funding from the European Union’s Horizon 2020 Research and Innovation Projects DeepCube and TREEADS, under Grant Agreement Numbers 101004188 and 101036926353 respectively