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000893826 0247_ $$2doi$$a10.1109/IGARSS47720.2021.9554656
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000893826 1001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b0$$eCorresponding author
000893826 1112_ $$aIEEE International Geoscience and Remote Sensing Symposium$$cBrussels$$d2021-07-12 - 2021-07-16$$gIGARSS 2021$$wBelgium
000893826 245__ $$aPractice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing
000893826 260__ $$bIEEE$$c2021
000893826 29510 $$a2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN 978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554656
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000893826 520__ $$aUsing computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent years. The continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., ’big data’) requires powerful computing resources with equally increasing performance. This paper reviews recent advances in High-Performance Computing (HPC), Cloud Computing (CC), and Quantum Computing (QC) applied to RS problems. It thus represents a snapshot of the state-of-the-art in ML in the context of the most recent developments in those computing areas, including our lessons learned over the last years. Our paper also includes some recent challenges and good experiences by using Europe's fastest supercomputer for hyper-spectral and multi-spectral image analysis with state-of-the-art data analysis tools. It offers a thoughtful perspective of the potential and emerging challenges of applying innovative computing paradigms to RS problems.
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000893826 536__ $$0G:(EU-Grant)951733$$aAISee - AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
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000893826 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b1
000893826 7001_ $$0P:(DE-HGF)0$$aBenediktsson, Jon Atli$$b2
000893826 773__ $$a10.1109/IGARSS47720.2021.9554656
000893826 8564_ $$uhttps://juser.fz-juelich.de/record/893826/files/Morris_Riedel_IGARSS_2021.pdf$$yOpenAccess
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