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| Home > Publications database > Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing > print |
| 001 | 893826 | ||
| 005 | 20250310131245.0 | ||
| 024 | 7 | _ | |a 10.1109/IGARSS47720.2021.9554656 |2 doi |
| 024 | 7 | _ | |a 2128/31337 |2 Handle |
| 024 | 7 | _ | |a WOS:001250139801210 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-02865 |
| 100 | 1 | _ | |a Riedel, Morris |0 P:(DE-Juel1)132239 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a IEEE International Geoscience and Remote Sensing Symposium |g IGARSS 2021 |c Brussels |d 2021-07-12 - 2021-07-16 |w Belgium |
| 245 | _ | _ | |a Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing |
| 260 | _ | _ | |c 2021 |b IEEE |
| 295 | 1 | 0 | |a 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN 978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554656 |
| 300 | _ | _ | |a 1571-1582 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1635431415_13275 |2 PUB:(DE-HGF) |
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| 520 | _ | _ | |a Using 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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| 536 | _ | _ | |a AISee - AI- and Simulation-Based Engineering at Exascale (951733) |0 G:(EU-Grant)951733 |c 951733 |f H2020-INFRAEDI-2019-1 |x 1 |
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| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 1 |
| 700 | 1 | _ | |a Benediktsson, Jon Atli |0 P:(DE-HGF)0 |b 2 |
| 773 | _ | _ | |a 10.1109/IGARSS47720.2021.9554656 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893826/files/Morris_Riedel_IGARSS_2021.pdf |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2021 |
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