AnySat: An Earth Observation Model
for Any Resolutions, Scales, and Modalities

Guillaume Astruc, Nicolas Gonthier, Clement Mallet, Loic Landrieu

CVPR 2025 (Highlight)

Code

Paper

Models

Introducing AnySat

AnySat is a multimodal model based on joint embedding predictive architecture (JEPA) and resolution-adaptive spatial encoders, allowing us in a self-supervised manner to handle:

📏 Multiple Scales

From local to global observations

🔍 Various Resolutions

Spatial, spectral, and temporal

🛰️ Different Modalities

Multiple sensor combinations

💡

Key Innovations

🔄

Shared Architecture

75% of parameters shared across all modalities and scales, enabling efficient multi-modal learning

🎯

Scale-Adaptive Design

Modified JEPA learning scheme with scale-adaptive spatial encoders for multi-resolution processing

🌐

Universal Compatibility

Handles data from 0.2m to 500m resolution, 3-12 channels, and areas from 0.3 to 2600 hectares

🌍

Datasets

We compile GeoPlex, a collection of multimodal datasets with varying characteristics to a single powerful model on these diverse datasets simultaneously. We argue quality and diversity over quantity.

5 Diverse Datasets

11 Distinct Sensors

0.2-250m Resolution Range

0.4-160ha Sample Areas

🏗️

Architecture

Scale-Adaptive JEPA Design

AnySat employs a novel Joint Embedding Predictive Architecture (JEPA) that adapts to multiple spatial scales and resolutions. Key components include:

Resolution-adaptive spatial encoders
Multi-modal fusion mechanism
Scale-aware prediction heads
Self-supervised learning framework

📊

Results

⚡

Quick Start


import torch
import anysat

# Load the model
anysat_B = torch.hub.load('gastruc/anysat', 'anysat_B')

# Prepare your data
data = {
    'naip':# NAIP single image B, 3, 24*H, 24*W
    's2':# Sentinel-2 time series B, T1, 10, 3*H, 3*W
    'alos':# ALOS-2 time series B, T2, 3, H, W
}

# Extract features
features = AnySat(data, patch_size=20)     # patch_size in meters

Bibtex

@article{astruc2024anysat,
    title={{AnySat: An Earth} Observation Model for Any Resolutions, Scales, and Modalities},
    author={Astruc, Guillaume and Gonthier, Nicolas and Mallet, Clement and Landrieu, Loic},
    journal={arXiv preprint arXiv:2412.14123},
    year={2024}
 }

Acknowledgements

This work was granted access to the HPC resources of IDRIS under the allocations AD011014719 and AD011014286R1 made by GENCI. We thank Jordi Inglada, Antoine Labatie, Dimitris Samaras, Yohann Perron, Vincent Lepetit for inspiring discussions and valuable feedback.

AnySat: An Earth Observation Modelfor Any Resolutions, Scales, and Modalities