Machine Learning API for Earth Observation Data Cubes Built on openEO

A new arXiv preprint proposes an API that bridges Earth Observation data cubes, which store imagery as spatio-temporal arrays, and the tabular or tensor formats that machine learning models expect. Built on the openEO standard, the interface aims to replace platform-specific preprocessing steps so the same ML workflow can run across different EO infrastructures. The work targets researchers who want to train and apply models on satellite data without writing custom conversion code for each provider.