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.

本文介绍了如何使用分割深度学习模型预测全球火灾烧伤面积在亚季节时标下的出现情况，并提出了开放访问的全球分析数据立方体，以包含与季节性和亚季节性火灾驱动因素（气候、植被、海洋指数、人类相关变量）有关的各种变量，以及 2001-2021 年历史燃烧面积和野火排放。