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@ARTICLE{Szwarcman:1050065,
      author       = {Szwarcman, Daniela and Roy, Sujit and Fraccaro, Paolo and
                      Gíslason, Orsteinn Elí and Blumenstiel, Benedikt and
                      Ghosal, Rinki and De Oliveira, Pedro Henrique and Almeida,
                      Joao Lucas de Sousa and Sedona, Rocco and Kang, Yanghui and
                      Chakraborty, Srija and Wang, Sizhe and Gomes, Carlos and
                      Kumar, Ankur and Gaur, Vishal and Truong, Myscon and Godwin,
                      Denys and Khallaghi, Sam and Lee, Hyunho and Hsu, Chia-Yu
                      and Asanjan, Ata Akbari and Mujeci, Besart and Shidham,
                      Disha and Balogun, Rufai Omowunmi and Kolluru, Venkatesh and
                      Keenan, Trevor and Arevalo, Paulo and Li, Wenwen and
                      Alemohammad, Hamed and Olofsson, Pontus and Mayer, Timothy
                      and Hain, Christopher and Kennedy, Robert and Zadrozny,
                      Bianca and Bell, David and Cavallaro, Gabriele and Watson,
                      Campbell and Maskey, Manil and Ramachandran, Rahul and
                      Moreno, Juan Bernabe},
      title        = {{P}rithvi-{EO}-2.0: {A} {V}ersatile {M}ulti-{T}emporal
                      {F}oundation {M}odel for {E}arth {O}bservation
                      {A}pplications},
      journal      = {IEEE transactions on geoscience and remote sensing},
      volume       = {64},
      issn         = {0018-9413},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-05777},
      pages        = {4400120},
      year         = {2025},
      abstract     = {This paper presents Prithvi-EO-2.0, a new geospatial
                      foundation model that offers significant improvements over
                      its predecessor, Prithvi-EO-1.0. Trained on 4.2 million
                      global time series samples from NASA’s Harmonized Landsat
                      and Sentinel-2 data archive at 30-m resolution, the new
                      model incorporates temporal and location embeddings for
                      enhanced performance across various geospatial tasks.
                      Through extensive benchmarking with GEO-Bench, the model
                      outperforms the previous Prithvi-EO model by $8\%$ across a
                      range of tasks. It also outperforms six other geospatial
                      foundation models when benchmarked on remote sensing tasks
                      from different domains and resolutions (i.e. from 0.1 m to
                      15 m). The results demonstrate the versatility of the model
                      in both classical Earth observation and high-resolution
                      applications. Early involvement of end-users and subject
                      matter experts (SMEs) allowed constant feedback on model and
                      dataset design, enabling customization across diverse
                      SME-led applications in disaster response, land cover and
                      crop mapping, and ecosystem dynamics monitoring.
                      Prithvi-EO-2.0 is available as an open-source model on
                      Hugging Face and IBM TerraTorch, with additional resources
                      on GitHub. The project exemplifies the Trusted Open Science
                      approach embraced by all involved organizations.},
      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/TGRS.2025.3642610},
      url          = {https://juser.fz-juelich.de/record/1050065},
}