Advancements in Earth system science have seen a surge in diverse datasets.
Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this
influx of high-dimensional data. ESDCs offer a structured, intuitive framework
for data analysis, organising information within spatio-temporal grids. The
structured nature of ESDCs unlocks significant opportunities for Artificial
Intelligence (AI) applications. By providing well-organised data, ESDCs are
ideally suited for a wide range of sophisticated AI-driven tasks. An automated
framework for creating AI-focused ESDCs with minimal user input could
significantly accelerate the generation of task-specific training data. Here we
introduce cubo, an open-source Python tool designed for easy generation of
AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC)
that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates
ESDCs, requiring only central coordinates, spatial resolution, edge size, and
time range.

地球系统科学中的进展带来了各种各样的数据集，而地球系统数据立方体 (ESDCs) 的引入有效地处理了这些高维数据的涌入。ESDCs 提供了一个结构化、直观的数据分析框架，将信息组织在时空网格中。ESDCs 的结构化特性为人工智能 (AI) 应用提供了重大机遇。通过提供良好组织的数据，ESDCs 非常适合各种复杂的 AI 驱动任务。一个自动化的框架可以通过最少的用户输入来创建针对 AI 的 ESDCs，从而加速特定任务训练数据的生成。本文介绍了 cubo，这是一个用于轻松生成以 AI 为焦点的 ESDCs 的开源 Python 工具。cubo 利用存储为云优化地理标记格式 (COGs) 的时空资产目录 (STAC) 中的集合，以中心坐标、空间分辨率、边缘大小和时间范围为参数高效地创建 ESDCs。