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@ARTICLE{Sharma:1029392,
      author       = {Sharma, Surbhi and Sedona, Rocco and Riedel, Morris and
                      Cavallaro, Gabriele and Paris, Claudia},
      title        = {{S}en4{M}ap: {A}dvancing {M}apping with {S}entinel-2 by
                      {P}roviding {D}etailed {S}emantic {D}escriptions and
                      {C}ustomizable {L}and-{U}se and {L}and-{C}over {D}ata},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {17},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05100},
      pages        = {13893 - 13907},
      year         = {2024},
      abstract     = {This paper presents Sen4Map, a large-scale benchmark
                      dataset designed to enhance the capability of generating
                      land-cover maps using Sentinel-2 data. Comprising
                      non-overlapping 64×64 patches extracted from Sentinel-2
                      time series images, the dataset spans 335,125 geo-tagged
                      locations across the European Union. These locations are
                      associated with detailed land-cover and land-use information
                      gathered by expert surveyors in 2018. Unlike most existing
                      large datasets available in the literature, the presented
                      database provides: (1) a detailed description of the
                      land-cover and land-use properties of each sampled area; (2)
                      independence of scale, as it is associated with reference
                      data collected in-situ by expert surveyors; (3) the ability
                      to test both temporal and spatial classification approaches
                      because of the availability of time series of 64×64 patches
                      associated with each labeled sample; and (4) samples were
                      collected following a stratified random sample design to
                      obtain a statistically representative spatial distribution
                      of land-cover classes throughout the European Union. To
                      showcase the properties and challenges offered by Sen4Map,
                      we benchmarked the current state-of-the-art land-cover
                      classification approaches. The dataset and code can be
                      downloaded at: https://datapub.fz-juelich.de/sen4map.},
      cin          = {JSC},
      ddc          = {520},
      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) /
                      Verbundprojekt: ADMIRE - Adaptives Datenmanagement für das
                      Exascale-Computing (16HPC008) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)956748 / G:(BMBF)16HPC008
                      / G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001294364400012},
      doi          = {10.1109/JSTARS.2024.3435081},
      url          = {https://juser.fz-juelich.de/record/1029392},
}