On-Demand Earth System Data Cubes

TL;DR

This paper introduces cubo, an open-source Python tool that efficiently creates AI-focused Earth System Data Cubes from geospatial data, facilitating advanced data analysis and AI applications in Earth sciences.

Contribution

The paper presents cubo, a novel automated framework for generating AI-ready Earth System Data Cubes with minimal user input, enhancing data accessibility and usability.

Findings

Cubo efficiently creates ESDCs from COGs with minimal input.

The framework accelerates AI data preparation in Earth sciences.

Open-source implementation promotes widespread adoption.

Abstract

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.