TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting

1 code implementation • 19 Jun 2023 • Ioannis Prapas, Nikolaos Ioannis Bountos, Spyros Kondylatos, Dimitrios Michail, Gustau Camps-Valls, Ioannis Papoutsis

To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections.

Management

Deep Learning for Global Wildfire Forecasting

no code implementations • 1 Nov 2022 • Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos, Ilektra Karasante, Eleanna Panagiotou, Lazaro Alonso, Charalampos Davalas, Dimitrios Michail, Nuno Carvalhais, Ioannis Papoutsis

We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.

Image Segmentation Semantic Segmentation

Deep Learning Methods for Daily Wildfire Danger Forecasting

no code implementations • 4 Nov 2021 • Ioannis Prapas, Spyros Kondylatos, Ioannis Papoutsis, Gustau Camps-Valls, Michele Ronco, Miguel-Ángel Fernández-Torres, Maria Piles Guillem, Nuno Carvalhais

Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability.