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@ARTICLE{Longp:1050134,
      author       = {Longépé, Nicolas and Alemohammad, Hamed and Anghelea,
                      Anca and Brunschwiler, Thomas and Camps-Valls, Gustau and
                      Cavallaro, Gabriele and Chanussot, Jocelyn and Delgado, Jose
                      Manuel and Demir, Begüm and Dionelis, Nikolaos and
                      Fraccaro, Paolo and Jungbluth, Anna and Kennedy, Robert E.
                      and Marsocci, Valerio and Ramasubramanian, Muthukumaran and
                      Ramos-Pollan, Raul and Roy, Sujit and Sümbül, Gencer and
                      Tuia, Devis and Zhu, Xiao Xiang and Ramachandran, Rahul},
      title        = {{E}arth {A}ction in {T}ransition: {H}ighlights {F}rom the
                      2025 {ESA}–{NASA} {I}nternational {W}orkshop on {AI}
                      {F}oundation {M}odels for {EO} [{S}pace-{A}gencies]},
      journal      = {IEEE geoscience and remote sensing magazine},
      volume       = {13},
      number       = {4},
      issn         = {2473-2397},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-05839},
      pages        = {457 - 462},
      year         = {2025},
      abstract     = {Over 850 people joined the first International Workshop on
                      AI Foundation Model (FM) for Earth Observation (EO)
                      co-organized by ESA and NASA 5-7 May 2025. Hosted at ESRIN
                      (ESA’s Earth Observation Center, Italy), the event
                      welcomed around 300 people on site, and an additional 550
                      online, with the promise that FMs can revolutionize EO and
                      Earth sciences. The workshop marked a pivotal moment in
                      aligning the EO and FM communities, fostering a shared
                      commitment to developing open and trustworthy tools that
                      support science discovery, operational applications, and
                      prescriptive analytics. EO data is massive, complex and high
                      dimensional requiring specific yet scalable AI
                      architectures. The workshop emphasized the need for training
                      and architecture enabling interpretability, explainability,
                      and physical consistency. Coordination should be
                      strengthened to minimize redundant development and to better
                      leverage collective expertise. The focus has shifted from
                      prototyping to real-world deployment, with FMs needing
                      further design for integration into digital twins,
                      dashboards, and edge platforms. Transparent benchmarking and
                      user-driven evaluation are key to guiding model development
                      and decision-making. In addition, parameter-efficient
                      adaptation, neural compression, and embedding-based
                      workflows offer promising paths for scaling EO analytics.
                      While FMs show promise, their effectiveness remains
                      context-dependent. The community debated whether to pursue
                      universal models, specialized solutions, or mixtures of
                      experts. The workshop envisioned the future of agentic AI in
                      EO, with multi-agent system powered by EO FMs and
                      vision-language models, that can dynamically reason and act
                      on EO data. This shift from static pipelines to adaptive,
                      smarter systems could redefine the future of EO. This paper
                      summarizes key discussions and concludes with
                      thought-provoking remarks.},
      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)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1109/MGRS.2025.3592035},
      url          = {https://juser.fz-juelich.de/record/1050134},
}