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@INPROCEEDINGS{Riedel:893826,
      author       = {Riedel, Morris and Cavallaro, Gabriele and Benediktsson,
                      Jon Atli},
      title        = {{P}ractice and {E}xperience in {U}sing {P}arallel and
                      {S}calable {M}achine {L}earning in {R}emote {S}ensing from
                      {HPC} {O}ver {C}loud to {Q}uantum {C}omputing},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-02865},
      pages        = {1571-1582},
      year         = {2021},
      comment      = {2021 IEEE International Geoscience and Remote Sensing
                      Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN
                      978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554656},
      booktitle     = {2021 IEEE International Geoscience and
                       Remote Sensing Symposium IGARSS :
                       [Proceedings] - IEEE, 2021. - ISBN
                       978-1-6654-0369-6 -
                       doi:10.1109/IGARSS47720.2021.9554656},
      abstract     = {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.},
      month         = {Jul},
      date          = {2021-07-12},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium, Brussels
                       (Belgium), 12 Jul 2021 - 16 Jul 2021},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / AISee - AI- and
                      Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001250139801210},
      doi          = {10.1109/IGARSS47720.2021.9554656},
      url          = {https://juser.fz-juelich.de/record/893826},
}