[Research Article] Towards an AI Cube for EO data inference in a distributed infrastructure
image: Overview of an AI Cube framework
Credit: Big Earth Data
A new study published in Big Earth Data proposes an AI cube framework to enhance geospatial data cube analytics by integrating GeoAI models for large-scale Earth Observation processing. The approach improves model selection efficiency and inference performance, advancing the development of AI-ready spatial data infrastructures.
Citation
Yue, P., & Wang, K. (2025). Towards an AI Cube for EO data inference in a distributed infrastructure. Big Earth Data, 1–39. https://doi.org/10.1080/20964471.2025.2585733https://doi.org/10.1080/20964471.2025.2585733
Abstract
Geospatial data cubes show great promise to facilitate integrated and efficient analysis of big Earth Observation (EO) data. Existing geospatial data cube processing focuses more on traditional geoprocessing algorithms. It is still unknown how to enhance cube analytics with geospatial artificial intelligence (GeoAI) models. The paper presents an AI cube approach to improve the cube capabilities from geospatial data storage and processing to the cube inference. The approach includes the proposal of a GeoAI model warehouse using a cube organization, a matchmaker for model selection, and a cube inference pipeline. It is developed using the on-the-fly and batch modes of model inference in a cube infrastructure-based EO cloud computing platform, the Open Geospatial Engine (OGE). The implementation illustrates that the approach enhances traditional data cubes from physical models to GeoAI analysis and contributes to the development of an AI-ready Spatial Data Infrastructure (SDI). The on-demand model matchmaker and parallel inference improve the performance of GeoAI inference by achieving the best accuracy from available models and reducing the inference time by over 80%.
#Earth observation #geospatial data cube #artificial intelligence (AI) #deep learning (DL) #AI cube
Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth’s systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.