On-Demand Earth System Data Cubes

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.

Publication
arXiv
David Montero
David Montero
PhD candidate / Earth System Data Science
Chaonan Ji
Chaonan Ji
Postdoctoral fellow / Earth System Data Science
Guido Kraemer
Guido Kraemer
Postdoctoral associate / Earth System Data Science
Maximilian Söchting
Maximilian Söchting
PhD candidate / Earth System Data Science
Khalil Teber
Khalil Teber
PhD candidate / Earth System Data Science
Miguel D. Mahecha
Miguel D. Mahecha
Professor for Earth System Data Science