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@ARTICLE{Sedona:1018223,
      author       = {Sedona, Rocco and Paris, Claudia and Ebert, Jan and Riedel,
                      Morris and Cavallaro, Gabriele},
      title        = {{T}oward the {P}roduction of {S}patiotemporally
                      {C}onsistent {A}nnual {L}and {C}over {M}aps {U}sing
                      {S}entinel-2 {T}ime {S}eries},
      journal      = {IEEE geoscience and remote sensing letters},
      volume       = {20},
      issn         = {1545-598X},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-04619},
      pages        = {1 - 5},
      year         = {2023},
      abstract     = {Land cover (LC) maps generated by the classification of
                      remote-sensing (RS) data allow for monitoring Earth
                      processes and the dynamics of objects and phenomena. For
                      accurate LC variability quantification in environmental
                      monitoring, maps need to be spatiotemporally consistent,
                      continually updated, and indicate permanent changes.
                      However, producing frequent and spatiotemporally consistent
                      LC maps is challenging because it involves balancing the
                      need for temporal consistency with the risk of missing real
                      changes. In this work, we propose a scalable and
                      semiautomatic method for generating annual LC maps with
                      labels that are consistently applied from one year to the
                      next. It uses a Transformer deep-learning (DL) model as a
                      classifier, which is trained on satellite time series (TS)
                      of images using high performance computing (HPC). The
                      trained model can generate stable maps by shifting the
                      prediction window along the temporal direction. The
                      effectiveness of the proposed approach is tested
                      qualitatively and quantitatively on a multiannual Sentinel-2
                      dataset acquired over a three-year period in a study area
                      located in the southern Italian Alps.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5112 - Cross-Domain
                      Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
                      (POF4-511) / RAISE - Research on AI- and Simulation-Based
                      Engineering at Exascale (951733) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(EU-Grant)951733 / G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001105671500009},
      doi          = {10.1109/LGRS.2023.3329428},
      url          = {https://juser.fz-juelich.de/record/1018223},
}