Abstract
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 create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, 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 2001-2021. 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 motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.
Add the full text or supplementary notes for the publication here using Markdown formatting.
Ioannis Prapas
PhD Candidate
My research interests include Deep Learning, Earth Observation, Wildfire Forecasting, Modeling Earth System Dynamics.
Akanksha Ahuja
Research scientist
Spyros Kondylatos
PhD Candidate
My research interests include Deep Learning, Bayesian Deep Learning, Earth Observation, Natural Hazards, Wildfires Forecasting
Dimitrios Michail
Associate Professor @ HUA
Adjunct Researcher @ OrionLab
My research interests include Graph Algorithms, Graph Mining, Machine Learning, Algorithm Engineering.
Ioannis Papoutsis
Head of Orion Lab
Assistant Professor of Artificial Intelligence for Earth Observation @ NTUA
Adjunct Researcher @ NOA
Earth Observation, Machine Learning, Natural Hazard Monitoring