TY - JOUR
T1 - ExtremeEarth meets satellite data from space
AU - Hagos, Desta Haileselassie
AU - Kakantousis, Theofilos
AU - Vlassov, Vladimir
AU - Sheikholeslami, Sina
AU - Wang, Tianze
AU - Dowling, Jim
AU - Paris, C.
AU - Marinelli, Daniele
AU - Weikmann, Giulio
AU - Bruzzone, Lorenzo
AU - Khaleghian, Salman
AU - Krmer, Thomas
AU - Eltoft, Torbjorn
AU - Marinoni, Andrea
AU - Pantazi, Despina-Athanasia
AU - Stamoulis, Georgios
AU - Bilidas, Dimitris
AU - Papadakis, George
AU - Mandilaras, George
AU - Koubarakis, Manolis
AU - Troumpoukis, Antonis
AU - Konstantopoulos, Stasinos
AU - Muerth, Markus
AU - Appel, Florian
AU - Fleming, Andrew H
AU - Cziferszky, Andreas
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2021/8/26
Y1 - 2021/8/26
N2 - 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.
AB - 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.
KW - ITC-CV
U2 - 10.1109/JSTARS.2021.3107982
DO - 10.1109/JSTARS.2021.3107982
M3 - Article
VL - 14
SP - 9038
EP - 9063
JO - IEEE Journal of selected topics in applied earth observations and remote sensing
JF - IEEE Journal of selected topics in applied earth observations and remote sensing
SN - 1939-1404
ER -