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.

介绍 Mesogeos，这是一个在地中海地区用于野火建模的多功能大型数据集。该数据集包含了表征野火驱动因素（气象、植被、人类活动）和历史记录的变量。数据集的结构为 1km x 1km x 1-day 像素的网格，在进行机器学习的野火建模任务时具有巨大的潜力，并且提供了两个机器学习模型的准备数据集，用于短期野火危险预测和极限燃烧区域估计。通过发布数据集和创建机器学习模型的代码，我们鼓励社区利用该数据集的巨大潜力，为中地中海地区日益增长的野火风险采取额外的措施。

Mesogeos：地中海地区数据驱动的野火多用途数据集

Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in  the Mediterranean

Influenced by the advances in data and computing, the scientific practice
increasingly involves machine learning and artificial intelligence driven
methods which requires specialized capabilities at the system-, science- and
service-level in addition to the conventional large-capacity supercomputing
approaches. The latest distributed architectures built around the composability
of data-centric applications led to the emergence of a new ecosystem for
container coordination and integration. However, there is still a divide
between the application development pipelines of existing supercomputing
environments, and these new dynamic environments that disaggregate fluid
resource pools through accessible, portable and re-programmable interfaces. New
approaches for dynamic composability of heterogeneous systems are needed to
further advance the data-driven scientific practice for the purpose of more
efficient computing and usable tools for specific scientific domains. In this
paper, we present a novel approach for using composable systems in the
intersection between scientific computing, artificial intelligence (AI), and
remote sensing domain. We describe the architecture of a first working example
of a composable infrastructure that federates Expanse, an NSF-funded
supercomputer, with Nautilus, a Kubernetes-based GPU geo-distributed cluster.
We also summarize a case study in wildfire modeling, that demonstrates the
application of this new infrastructure in scientific workflows: a composed
system that bridges the insights from edge sensing, AI and computing
capabilities with a physics-driven simulation.

本文介绍了一种新的动态组合异构系统的方法，以进一步推进数据驱动的科学实践，以提高计算效率和特定科学领域的可用工具，并概述了一项以可组合基础架构为基础的案例研究，展示了火灾建模的应用。