AnySat: An Earth Observation Model for Any Resolutions, Scales, and
  Modalities
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.
6763cdf3725a6df3137cc526
Guillaume Astruc
claimed_verified
2024-12-19T18:01:03.354Z
662b7fba68ed7bbf40bfb0df
/avatars/c04f8fd10bc78a6613d5a0c74bda3cef.svg
Guillaume Astruc
user
g-astruc
6763cdf3725a6df3137cc527
Nicolas Gonthier
admin_assigned
2024-12-20T09:53:56.410Z
6582b7dd75754a803e484487
https://cdn-avatars.huggingface.co/v1/production/uploads/6582b7dd75754a803e484487/Eh68fF9Yi6cG5Xacrguc-.jpeg
Nicolas Gonthier
user
NGonthier
6763cdf3725a6df3137cc528
Clement Mallet
6763cdf3725a6df3137cc529
Loic Landrieu
claimed_verified
2025-04-03T13:33:51.459Z
64f73dbe61afe1d3356b10cc
/avatars/bacf3c01515d06dc5f21f6f732f52b01.svg
landrieu
user
loicland
AnySat: An Earth Observation Model for Any Resolutions, Scales, and
  Modalities
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.
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.
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