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@PHDTHESIS{Sedona:1020577,
      author       = {Sedona, Rocco},
      title        = {{S}calable {D}eep {L}earning for {R}emote {S}ensing with
                      {H}igh {P}erformance {C}omputing},
      school       = {University of Iceland},
      type         = {Dissertation},
      reportid     = {FZJ-2024-00272},
      isbn         = {978-9935-9697-8-1},
      pages        = {139 p.},
      year         = {2023},
      note         = {Dissertation, University of Iceland, 2023},
      abstract     = {Advances in remote sensing (RS) missions in recent decades
                      have greatly increased the volume of data that is
                      continually acquired and made available to end users, who
                      can utilize it in a variety of Earth observation (EO)
                      applications. land cover (LC) maps play a key role in
                      monitoring the Earth’s surface, providing scientists and
                      policymakers with an accurate view of the evolution of the
                      landscape and helping them address pressing questions, from
                      efficient resource planning to resilience to climate change.
                      Due to the use of classical machine learning (ML) and more
                      recently of deep learning (DL) methods, the information
                      content of RS data can be exploited to an unprecedented
                      degree, fostering research, development, and deployment of
                      workloads to address open challenges for EO applications,
                      including LC classification. However, the larger size of the
                      datasets needed to train state-of-the-art (SotA) DL models
                      and the need to utilize them at scale increases the time to
                      deployment, which can hinder their effective utilization.
                      Adopting strategies for distributed deep learning (DDL) on
                      high performance computing (HPC) systems provides the
                      opportunity to speed up the training of the models, allowing
                      faster development times for researchers. Since space
                      agencies operate a variety of missions, data acquired by
                      different sensors can be used to increase the temporal
                      resolution at which a certain area is observed, with
                      potential improvements in the accuracy of the ML/DL models.
                      The thesis objectives are formulated with these premises in
                      mind and were investigated using a combination of
                      methodologies to exploit the dedicated resources of HPC
                      systems, contributing to addressing new questions on the
                      adoption of DDL methods for EO applications and to
                      familiarize the RS community with such approaches, which can
                      be of great},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / ADMIRE - Adaptive
                      multi-tier intelligent data manager for Exascale (956748) /
                      RAISE - Research on AI- and Simulation-Based Engineering at
                      Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)956748 /
                      G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.34734/FZJ-2024-00272},
      url          = {https://juser.fz-juelich.de/record/1020577},
}