Towards a Unified Copernicus Foundation Model for Earth Vision
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.
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Yi Wang
claimed_verified
2025-03-26T20:44:12.292Z
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Yi Wang
user
wangyi111
67e3d0ac304f166b665e4a68
Zhitong Xiong
claimed_verified
2025-07-01T10:15:28.399Z
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zhitong
user
earthflow
67e3d0ac304f166b665e4a69
Chenying Liu
67e3d0ac304f166b665e4a6a
Adam J. Stewart
67e3d0ac304f166b665e4a6b
Thomas Dujardin
67e3d0ac304f166b665e4a6c
Nikolaos Ioannis Bountos
67e3d0ac304f166b665e4a6d
Angelos Zavras
claimed_verified
2025-04-08T06:57:37.519Z
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Angelos Zavras
user
azavras
67e3d0ac304f166b665e4a6e
Franziska Gerken
67e3d0ac304f166b665e4a6f
Ioannis Papoutsis
67e3d0ac304f166b665e4a70
Laura Leal-Taixé
67e3d0ac304f166b665e4a71
Xiao Xiang Zhu
Towards a Unified Copernicus Foundation Model for Earth Vision
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.
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.
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