@article{3068544,
    title = "ExtremeEarth meets satellite data from space",
    author = "Hagos, D.H. and Kakantousis, T. and Vlassov, V. and Sheikholeslami, S. and Wang, T. and Dowling, J. and Paris, C. and Marinelli, D. and Weikmann, G. and Bruzzone, L. and Khaleghian, S. and Kraemer, T. and Eltoft, T. and Marinoni, A. and Pantazi, D.-A. and Stamoulis, G. and Bilidas, D. and Papadakis, G. and Mandilaras, G. and Koubarakis, M. and Troumpoukis, A. and Konstantopoulos, S. and Muerth, M. and Appel, F. and Fleming, A. and Cziferszky, A.",
    journal = "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing",
    year = "2021",
    volume = "14",
    pages = "9038-9063",
    publisher = "Institute of Electrical and Electronics Engineers, Inc. (IEEE)",
    issn = "1939-1404",
    doi = "10.1109/JSTARS.2021.3107982",
    keywords = "Classification (of information);  Computer architecture;  Data handling;  Deep learning;  Food supply;  Large dataset;  Learning systems;  Remote sensing;  Space platforms, Cutting edge technology;  Data informations;  European Space Agency;  Learning architectures;  Scalable machine learning;  Software infrastructure;  Software platforms;  Training data sets, Learning algorithms, artificial intelligence;  food security;  polar region;  remote sensing;  satellite data;  Sentinel;  software;  spatial data",
    abstract = "Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)'s Thematic Exploitation Platforms (TEPs) and data information access service platforms with a data platform, namely Hopsworks, which enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification of Sentinel images. In this article, we present the software architecture of ExtremeEarth that aims at the development of scalable deep learning and geospatial analytics techniques for processing and analyzing petabytes of Copernicus data. The ExtremeEarth software infrastructure seamlessly integrates existing and novel software platforms and tools for storing, accessing, processing, analyzing, and visualizing large amounts of Copernicus data. New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed. These techniques and corresponding software presented in this article are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we present the integration of Hopsworks with the Polar and Food Security use cases and the flow of events for the products offered through the TEPs. © 2008-2012 IEEE."
}