title: AnySat: An Earth Observation Model for Any Resolutions, Scales, and
    Modalities
summary: Geospatial models must adapt to the diversity of Earth observation data in
  terms of resolutions, scales, and modalities. However, existing approaches
  expect fixed input configurations, which limits their practical applicability.
  We propose AnySat, a multimodal model based on joint embedding predictive
  architecture (JEPA) and resolution-adaptive spatial encoders, allowing us to
  train a single model on highly heterogeneous data in a self-supervised manner.
  To demonstrate the advantages of this unified approach, we compile GeoPlex, a
  collection of 5 multimodal datasets with varying characteristics and 11
  distinct sensors. We then train a single powerful model on these diverse
  datasets simultaneously. Once fine-tuned, we achieve better or near
  state-of-the-art results on the datasets of GeoPlex and 4 additional ones for
  5 environment monitoring tasks: land cover mapping, tree species
  identification, crop type classification, change detection, and flood
  segmentation. The code and models are available at
  https://github.com/gastruc/AnySat.
paper_authors._id: 6763cdf3725a6df3137cc526
paper_authors.name: Guillaume Astruc
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-19T18:01:03.354Z
paper_authors._id: 662b7fba68ed7bbf40bfb0df
paper_authors.avatarUrl: /avatars/c04f8fd10bc78a6613d5a0c74bda3cef.svg
paper_authors.fullname: Guillaume Astruc
paper_authors.type: user
paper_authors.user: g-astruc
paper_authors._id: 6763cdf3725a6df3137cc527
paper_authors.name: Nicolas Gonthier
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-20T09:53:56.410Z
paper_authors._id: 6582b7dd75754a803e484487
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6582b7dd75754a803e484487/Eh68fF9Yi6cG5Xacrguc-.jpeg
paper_authors.fullname: Nicolas Gonthier
paper_authors.type: user
paper_authors.user: NGonthier
paper_authors._id: 6763cdf3725a6df3137cc528
paper_authors.name: Clement Mallet
paper_authors._id: 6763cdf3725a6df3137cc529
paper_authors.name: Loic Landrieu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-03T13:33:51.459Z
paper_authors._id: 64f73dbe61afe1d3356b10cc
paper_authors.avatarUrl: /avatars/bacf3c01515d06dc5f21f6f732f52b01.svg
paper_authors.fullname: landrieu
paper_authors.type: user
paper_authors.user: loicland
paper_title: AnySat: An Earth Observation Model for Any Resolutions, Scales, and
    Modalities
paper_summary: Geospatial models must adapt to the diversity of Earth observation data in
  terms of resolutions, scales, and modalities. However, existing approaches
  expect fixed input configurations, which limits their practical applicability.
  We propose AnySat, a multimodal model based on joint embedding predictive
  architecture (JEPA) and resolution-adaptive spatial encoders, allowing us to
  train a single model on highly heterogeneous data in a self-supervised manner.
  To demonstrate the advantages of this unified approach, we compile GeoPlex, a
  collection of 5 multimodal datasets with varying characteristics and 11
  distinct sensors. We then train a single powerful model on these diverse
  datasets simultaneously. Once fine-tuned, we achieve better or near
  state-of-the-art results on the datasets of GeoPlex and 4 additional ones for
  5 environment monitoring tasks: land cover mapping, tree species
  identification, crop type classification, change detection, and flood
  segmentation. The code and models are available at
  https://github.com/gastruc/AnySat.
paper_ai_summary: Anysat uses joint embedding predictive architecture and resolution-adaptive spatial encoders to train a unified multimodal model on diverse Earth observation datasets, achieving state-of-the-art performance across various environment monitoring tasks.
paper_ai_keywords: joint embedding predictive architecture JEPA resolution-adaptive spatial encoders multimodal datasets GeoPlex self-supervised manner land cover mapping tree species identification crop type classification change detection flood segmentation