Hopsworks Research Paper

Deepcube: Explainable Ai Pipelines for Big Copernicus Data


Ioannis Papoutsis, Alkyoni Baglatzi, Souzana Touloumtzi, Markus Reichstein, Nuno Carvalhais, Fabian Gans, Gustau Camps-Valls, Maria Piles, Theofilos Kakantousis, Jim Dowling, Manolis Koubarakis, Dimitris Bilidas, Despina-Athanasia Pantazi, George Stamoulis, Christophe Demange, Léo-Gad Journel, Marco Bianchi, Chiara Gervasi, Alessio Rucci, Ioannis Tsampoulatidis, Eleni Kamateri, Tarek Habib, Alejandro Dıaz Bolıvar, Zisoula Ntasiou, Anastasios Paschalis


The H2020 DeepCube project leverages advances in the fields of Artificial Intelligence and Semantic Web to unlock the potential of Copernicus Big Data and contribute to the Digital Twin Earth initiative. DeepCube aims to address problems of high socio-environmental impact and enhance our understanding of Earth’s processes correlated with Climate Change. To achieve this, the project employs novel technologies, such as the Earth System Data Cube, the Semantic Cube, the Hopsworks platform for distributed deep learning, and visual analytics tools, integrating them into an open, cloud-interoperable platform. DeepCube will develop Deep Learning architectures that extend to non-conventional data, apply hybrid modeling for data-driven AI models that respect physical laws, and open up the Deep Learning black box with Explainable Artificial Intelligence and Causality.

© Hopsworks 2024. All rights reserved. Various trademarks held by their respective owners.

Privacy Policy
Cookie Policy
Terms and Conditions