2 research outputs found
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire
modeling in the Mediterranean. Mesogeos integrates variables representing
wildfire drivers (meteorology, vegetation, human activity) and historical
records of wildfire ignitions and burned areas for 17 years (2006-2022). It is
designed as a cloud-friendly spatio-temporal dataset, namely a datacube,
harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The
datacube structure offers opportunities to assess machine learning (ML) usage
in various wildfire modeling tasks. We extract two ML-ready datasets that
establish distinct tracks to demonstrate this potential: (1) short-term
wildfire danger forecasting and (2) final burned area estimation given the
point of ignition. We define appropriate metrics and baselines to evaluate the
performance of models in each track. By publishing the datacube, along with the
code to create the ML datasets and models, we encourage the community to foster
the implementation of additional tracks for mitigating the increasing threat of
wildfires in the Mediterranean
Deep Learning for Global Wildfire Forecasting
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.Comment: Accepted at the NeurIPS 2022 workshop on Tackling Climate Change with
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