On-Demand Earth System Data Cubes
arxiv(2024)
摘要
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.
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