Machine learning API standardizes earth observation data cube processing
A Machine Learning API for Earth Observation Data Cubes Based on openEO
Machine Learning
Summary
Earth observation data, such as satellite images over time, is stored in complex 3D grids called data cubes. Machine learning normally works with simpler tables, causing problems when trying to analyze this data across different cloud systems. The authors created a new standard process that lets machine learning run consistently on these data cubes using a common interface called openEO. They showed this works with various algorithms and programming languages, but also found complete compatibility needs further work on how models are saved and run. This helps improve the sharing and reuse of machine learning workflows across platforms.
What this means in practice
- •For cloud platform engineers: Run identical machine learning workflows on diverse earth observation backends with a unified API to ensure consistent results.
- •For agricultural analytics teams: Deploy standardized crop type mapping models across multiple data processing platforms for scalable and reproducible agricultural monitoring.
Authors
Brian Pondi, Jonas Hurst, Rolf Simoes, Jonas Starke, Marius Appel, Edzer Pebesma
Abstract
Earth Observation (EO) data are increasingly organized as spatio-temporal data cubes, while machine learning (ML) methods operate on tabular feature matrices or structured tensor inputs. This mismatch forces platform-specific transformations that are difficult to reproduce or transfer across cloud infrastructures. The openEO specification provides a unified interface for EO data access and processing across heterogeneous backends, but lacks a standardized approach for ML integration. We propose a process-level ML specification for openEO structured into three stages: model initialization, model actions (training, tuning, inference, validation), and model management. It supports classical algorithms such as Random Forest and SVM, as well as deep learning architectures for time-series and spatial patch-based modeling, including TempCNN, Temporal Attention Encoders, and foundation model inference. Three prototype implementations in R and Python demonstrate feasibility across diverse technology stacks. A crop type mapping use case demonstrates cross-backend interoperability by submitting an identical process graph to independent R and Python backends and comparing predictions and evaluation metrics. Two further use cases demonstrate deep learning on time series and foundation model inference, each executed on a dedicated backend. The prototypes reveal, however, that full cross-backend portability requires deeper harmonization of serialization formats and execution semantics than the process level alone can enforce; backend library versions and preprocessing conventions outside the specification's boundary also affect reproducibility. Addressing both through explicit backend conformance profiles represents the most important near-term direction. The specification advances the reproducibility, portability, and accessibility of ML workflows on EO data cubes across cloud platforms.