The SeasFire Cube is an open access scientific datacube for seasonal fire forecasting around the globe. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.
It contains 21 years of data (2001-2021) in an 8-days time resolution and 0.25 degrees grid resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.
All versions are available for download on Zenodo.
SeasFire Cube v0.3: https://doi.org/10.5281/zenodo.8055879
SeasFire Cube v0.2: https://doi.org/10.5281/zenodo.7108392
SeasFire Cube v0.1: https://doi.org/10.5281/zenodo.6834585
Are you interested in learning how to use the SeasFire Cube? We have created a simple walkthrough to open the dataset, filter it based on location and time, and visualise its features.
Python tutorials: https://github.com/SeasFire/seasfire-datacube/tree/main/Python-Tutorials
ESA Φ-Lab Workshop on AI for Natural Hazard Management
|Akanksha Ahuja, National Observatory of Athens||Graph Neural Networks for Remote Sensing|
|Dr. Nuno Carvalhais, Max Planck Institute for Biogeochemistry||Bridging statistical learning and process-based modeling of the Earth system|
|Prof. Gustau Camps-Valls, Universitat de València||Causal Inference for Disaster Management|
|Spyros Kondylatos, National Observatory of Athens||Probabilistic Machine Learning for Disaster Management|
|Prof. Dimitrios Michail, Harokopeio University of Athens||Graph Neural Networks for Remote Sensing|
|Dr. Ioannis Papoutsis, National Observatory of Athens||Early Warning: Volcanic unrest detection|
| Dr. Michele Ronco, Universitat de València|
Cristiano De Nobili, Johanna Strebl, Giovanni Paolini, Pi School of AI
|Explainable AI for Wildfire Forecasting|
|Ioannis Prapas, National Observatory of Athens||Deep Learning for Rapid Landslide Detection|
|Ioannis Prapas, National Observatory of Athens||Deep Learning for Wildfire Danger Forecasting at Different Spatiotemporal Scales|
|Prof. Raúl Ramos, Universidad de Antioquia||Deep Learning Data Pipelines for Rapid Emergency Response|