AnySat: An Earth Observation Model for Any Resolutions, Scales, and
  Modalities
Authors: name: Guillaume Astruc, name: Nicolas Gonthier, name: Clement Mallet, name: Loic Landrieu

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.