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@ARTICLE{Gomes:1041099,
      author       = {Gomes, Carlos and Wittmann, Isabelle and Robert, Damien and
                      Jakubik, Johannes and Reichelt, Tim and Maurogiovanni,
                      Stefano and Vinge, Rikard and Hurst, Jonas and Scheurer,
                      Erik and Sedona, Rocco and Brunschwiler, Thomas and
                      Kesselheim, Stefan and Batic, Matej and Stier, Philip and
                      Wegner, Jan Dirk and Cavallaro, Gabriele and Pebesma, Edzer
                      and Marszalek, Michael and Belenguer-Plomer, Miguel A. and
                      Adriko, Kennedy and Fraccaro, Paolo and Kienzler, Romeo and
                      Briq, Rania and Benassou, Sabrina and Lazzarini, Michele and
                      Albrecht, Conrad M.},
      title        = {{L}ossy {N}eural {C}ompression for {G}eospatial
                      {A}nalytics: {A} review},
      journal      = {IEEE geoscience and remote sensing magazine},
      volume       = {13},
      number       = {3},
      issn         = {2473-2397},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-02140},
      pages        = {97-135},
      year         = {2025},
      note         = {This research is carried out as part of the Embed2Scale
                      project and is cofunded by the EU Horizon Europe program
                      under Grant Agreement 101131841. Additional funding for this
                      project has been provided by the Swiss State Secretariat for
                      Education, Research and Innovation and UK Research and
                      Innovation},
      abstract     = {Over the past decades, there has been an explosion in the
                      amount of available Earth observation (EO) data. The
                      unprecedented coverage of Earth’s surface and atmosphere
                      by satellite imagery has resulted in large volumes of data
                      that must be transmitted to ground stations, stored in data
                      centers, and distributed to end users. Modern Earth system
                      models (ESMs) face similar challenges, operating at high
                      spatial and temporal resolutions, producing petabytes of
                      data per simulated day. Data compression has gained
                      relevance over the past decade, with neural compression (NC)
                      emerging from deep learning and information theory, making
                      EO data and ESM outputs ideal candidates because of their
                      abundance of unlabeled data. In this review, we outline
                      recent developments in NC applied to geospatial data. We
                      introduce the fundamental concepts of NC, including seminal
                      works in its traditional applications to image and video
                      compression domains with a focus on lossy compression. We
                      discuss the unique characteristics of EO and ESM data,
                      contrasting them with “natural images,” and we explain
                      the additional challenges and opportunities they present.
                      Additionally, we review current applications of NC across
                      various EO modalities and explore the limited efforts in ESM
                      compression to date. The advent of self-supervised learning
                      (SSL) and foundation models (FMs) has advanced methods to
                      efficiently distill representations from vast amounts of
                      unlabeled data. We connect these developments to NC for EO,
                      highlighting the similarities between the two fields and
                      elaborate on the potential of transferring compressed
                      feature representations for machine-to-machine
                      communication. Based on insights drawn from this review, we
                      devise future directions relevant to applications in EO and
                      ESMs.},
      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) / Embed2Scale - Earth Observation $\&$ Weather
                      Data Federation with AI Embeddings (101131841)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(EU-Grant)101131841},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1109/MGRS.2025.3546527},
      url          = {https://juser.fz-juelich.de/record/1041099},
}