title: Towards a Unified Copernicus Foundation Model for Earth Vision
summary: Advances in Earth observation (EO) foundation models have unlocked the
  potential of big satellite data to learn generic representations from space,
  benefiting a wide range of downstream applications crucial to our planet.
  However, most existing efforts remain limited to fixed spectral sensors, focus
  solely on the Earth's surface, and overlook valuable metadata beyond imagery.
  In this work, we take a step towards next-generation EO foundation models with
  three key components: 1) Copernicus-Pretrain, a massive-scale pretraining
  dataset that integrates 18.7M aligned images from all major Copernicus Sentinel
  missions, spanning from the Earth's surface to its atmosphere; 2)
  Copernicus-FM, a unified foundation model capable of processing any spectral or
  non-spectral sensor modality using extended dynamic hypernetworks and flexible
  metadata encoding; and 3) Copernicus-Bench, a systematic evaluation benchmark
  with 15 hierarchical downstream tasks ranging from preprocessing to specialized
  applications for each Sentinel mission. Our dataset, model, and benchmark
  greatly improve the scalability, versatility, and multimodal adaptability of EO
  foundation models, while also creating new opportunities to connect EO,
  weather, and climate research. Codes, datasets and models are available at
  https://github.com/zhu-xlab/Copernicus-FM.
paper_authors._id: 67e3d0ac304f166b665e4a67
paper_authors.name: Yi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-26T20:44:12.292Z
paper_authors._id: 64cba974a81988d0734c9925
paper_authors.avatarUrl: /avatars/645c326ca38eb751144f356076cef60f.svg
paper_authors.fullname: Yi Wang
paper_authors.type: user
paper_authors.user: wangyi111
paper_authors._id: 67e3d0ac304f166b665e4a68
paper_authors.name: Zhitong Xiong
paper_authors._id: 67e3d0ac304f166b665e4a69
paper_authors.name: Chenying Liu
paper_authors._id: 67e3d0ac304f166b665e4a6a
paper_authors.name: Adam J. Stewart
paper_authors._id: 67e3d0ac304f166b665e4a6b
paper_authors.name: Thomas Dujardin
paper_authors._id: 67e3d0ac304f166b665e4a6c
paper_authors.name: Nikolaos Ioannis Bountos
paper_authors._id: 67e3d0ac304f166b665e4a6d
paper_authors.name: Angelos Zavras
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:57:37.519Z
paper_authors._id: 63f69d80b4c9a104f4b93ab1
paper_authors.avatarUrl: /avatars/9892b4b09d9b01626925933e12f10a93.svg
paper_authors.fullname: Angelos Zavras
paper_authors.type: user
paper_authors.user: azavras
paper_authors._id: 67e3d0ac304f166b665e4a6e
paper_authors.name: Franziska Gerken
paper_authors._id: 67e3d0ac304f166b665e4a6f
paper_authors.name: Ioannis Papoutsis
paper_authors._id: 67e3d0ac304f166b665e4a70
paper_authors.name: Laura Leal-Taixé
paper_authors._id: 67e3d0ac304f166b665e4a71
paper_authors.name: Xiao Xiang Zhu
paper_title: Towards a Unified Copernicus Foundation Model for Earth Vision
paper_summary: Advances in Earth observation (EO) foundation models have unlocked the
  potential of big satellite data to learn generic representations from space,
  benefiting a wide range of downstream applications crucial to our planet.
  However, most existing efforts remain limited to fixed spectral sensors, focus
  solely on the Earth's surface, and overlook valuable metadata beyond imagery.
  In this work, we take a step towards next-generation EO foundation models with
  three key components: 1) Copernicus-Pretrain, a massive-scale pretraining
  dataset that integrates 18.7M aligned images from all major Copernicus Sentinel
  missions, spanning from the Earth's surface to its atmosphere; 2)
  Copernicus-FM, a unified foundation model capable of processing any spectral or
  non-spectral sensor modality using extended dynamic hypernetworks and flexible
  metadata encoding; and 3) Copernicus-Bench, a systematic evaluation benchmark
  with 15 hierarchical downstream tasks ranging from preprocessing to specialized
  applications for each Sentinel mission. Our dataset, model, and benchmark
  greatly improve the scalability, versatility, and multimodal adaptability of EO
  foundation models, while also creating new opportunities to connect EO,
  weather, and climate research. Codes, datasets and models are available at
  https://github.com/zhu-xlab/Copernicus-FM.
paper_ai_summary: A next-generation Earth observation foundation model system includes a large pretraining dataset, a unified model using hypernetworks and metadata encoding, and an evaluation benchmark, enhancing scalability and multimodality in Earth observation.
paper_ai_keywords: Earth observation EO foundation models Copernicus-Pretrain Copernicus-FM Copernicus-Bench dynamic hypernetworks metadata encoding EO foundation models Satellite data scalability versatility multimodal adaptability weather climate research