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.