ExtremeEarth meets satellite data from space

Hagos, Desta Haileselassie; Kakantousis, Theofilos; Vlassov, Vladimir; Sheikholeslami, Sina; Wang, Tianze; Dowling, Jim; Paris, Claudia; Marinelli, Daniele; Weikmann, Giulio; Bruzzone, Lorenzo; Khaleghian, Salman; Krmer, Thomas; Eltoft, Torbjorn; Marinoni, Andrea; Pantazi, Despina-Athanasia; Stamoulis, Georgios; Bilidas, Dimitris; Papadakis, George; Mandilaras, George; Koubarakis, Manolis; Troumpoukis, Antonis; Konstantopoulos, Stasinos; Muerth, Markus; Appel, Florian; Fleming, Andrew H. ORCID: https://orcid.org/0000-0002-0143-4527; Cziferszky, Andreas ORCID: https://orcid.org/0000-0002-1330-6733. 2021 ExtremeEarth meets satellite data from space. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14. 9038-963. https://doi.org/10.1109/JSTARS.2021.3107982

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Official URL: https://ieeexplore.ieee.org/document/9523756

Abstract/Summary

Bringing together a number of cutting-edge technologies that range from storing extremely large volumesof 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 unprecedentedchallenges. 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, that 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 paper, 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 thispaper are to be integrated with and used in two ESA TEPs, namely Polar and Food Security TEPs. Furthermore, we presentthe integration of Hopsworks with the Polar and Food Securityuse cases and the flow of events for the products offered through the TEPs.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1109/JSTARS.2021.3107982
ISSN:	1939-1404
Additional Keywords:	Deep learning, Satellites, Monitoring, Sea ice, Geospatial analysis, Computer architecture, Data models
Date made live:	07 Sep 2021 10:43 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/531016

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1109/JSTARS.2021.3107982

ISSN:

1939-1404

Additional Keywords:

Deep learning, Satellites, Monitoring, Sea ice, Geospatial analysis, Computer architecture, Data models

Date made live:

07 Sep 2021 10:43 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/531016