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Deep Learning for Global Wildfire Forecasting
Ioannis Prapas · Akanksha Ahuja · Spyros Kondylatos · Ilektra Karasante · Lazaro Alonso · Lefki-Ioanna Panagiotou · Charalampos Davalas · Dimitrios Michail · Nuno Carvalhais · IOANNIS PAPOUTSIS
Event URL: https://www.climatechange.ai/papers/neurips2022/52 »

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects.In this work, we use deep learning to forecast the presence of global burned areas on a sub-seasonal scale. We present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2000-2021. 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. Our work paves the way towards improved anticipation of global wildfire patterns.

Author Information

Ioannis Prapas (University of Valencia, National Observatory of Athens)
Akanksha Ahuja (University of Cambridge)
Spyros Kondylatos (National Observatory of Athens)
Ilektra Karasante (National Observatory of Athens)
Lazaro Alonso (Max Planck Institute for Biogeochemistry)
Lefki-Ioanna Panagiotou (Harokopio University of Athens)
Charalampos Davalas (Harokopio University of Athens)
Dimitrios Michail (Harokopio University of Athens)
Nuno Carvalhais (Max Planck Institute for Biogeochemistry)
IOANNIS PAPOUTSIS (National Observatory of Athens)

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