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ExtremeEarth Meets Satellite Data From Space
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
Log Clocks, S-16440 Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-6779-7435
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-7236-4637
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2021 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 14, p. 9038-9063Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. Vol. 14, p. 9038-9063
Keywords [en]
Deep learning, Satellites, Monitoring, Sea ice, Geospatial analysis, Computer architecture, Data models, Artificial intelligence (AI), copernicus, earth observation (EO), extremeearth, food security, hopsworks, linked geospatial data, polar regions, remote sensing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-303190DOI: 10.1109/JSTARS.2021.3107982ISI: 000697823200006Scopus ID: 2-s2.0-85113868176OAI: oai:DiVA.org:kth-303190DiVA, id: diva2:1601997
Note

QC 20211011

Available from: 2021-10-11 Created: 2021-10-11 Last updated: 2023-03-06Bibliographically approved

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Hagos, Desta HaileselassieVlassov, VladimirSheikholeslami, SinaWang, Tianze

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Hagos, Desta HaileselassieVlassov, VladimirSheikholeslami, SinaWang, Tianze
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