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.
Chaonan Ji
Postdoctoral fellow / Earth System Data Science
My research interests include the classification of hyperspectral images from air and space, gradient analysis, and its application in urban areas. My current research interest is in the study and analysis of environmental responses to climate extremes.
Guido Kraemer
Postdoctoral associate / Earth System Data Science
My research interests include the interactions between society and biosphere. I have been working on the extraction of the global dynamics of ecosystems and society. I have an interest in using machine learning and multivariate statistics to understand the behavior of complex systems.
Maximilian Söchting
PhD candidate / Earth System Data Science
My research interests include computer graphics, interactive applications and data compression algorithms. I am working on an interactive visualization of remote sensing data in a collaboration between the Image and Signal Processing Group and the Earth System Data Science group.
