From optical sensors to microwave radars, leveraging the complementary strengths of remote sensing (RS) sensors is crucial for achieving dense spatio-temporal monitoring of our planet, but recent foundation models (FMs) are often specific to single sensors or to fixed combinations.
SMARTIES is a generic and versatile FM lifting sensor-dependent efforts and enabling scalability and generalization to diverse RS sensors: SMARTIES projects data from heterogeneous sensors into a shared spectrum-aware space, enabling the use of arbitrary combinations of bands both for training and inference. To obtain sensor-agnostic representations, SMARTIES was trained as a single, unified transformer model reconstructing masked multi-sensor data with cross-sensor token mixup, while modulating its feature representations to accept diverse sensors as input.
✨ Key Features
🛰️ Multi-Sensor Representations: SMARTIES enables sensor-agnostic processing of Earth observation data, including optical (e.g., Sentinel-2), radar (e.g., Sentinel-1), and sub-meter resolution RGB (e.g., Maxar) imagery and unseen ones in a zero-shot manner.
🌈 Spectrum-Aware Projections: SMARTIES projects data from heterogeneous sensors into a shared spectrum-aware space: given a specific sensor, each one of its bands is projected by projection layers specific to wavelength ranges.
⚡ Lightweight and Scalable: SMARTIES is designed to be lightweight and scalable, making it suitable for a wide range of remote sensing applications.
🔀 Flexible Band Combinations: SMARTIES can handle arbitrary combinations of spectral bands from different sensors, enabling flexible remote sensing applications.
🔄 Downstream Transfer: SMARTIES enables downstream transfer using a unified model across a diverse set of sensors and tasks, including scene classification, semantic segmentation, and multi-label classification.
SMARTIES Model Architecture
This repository contains the model weights of SMARTIES (ViT-B).
🧩 Using SMARTIES
SMARTIES is designed to be flexible and can be easily adapted to new datasets and sensors. You can easily use SMARTIES with a single line of code with Hugging Face transformer interface:
A Jupyter notebook is provided in SMARTIES GitHub page to show in detail how to use pretrained model weights. The details of SMARTIES are described in our paper, available on arXiv.
📣 Attribution
If you use SMARTIES, please cite the paper:
@article{smarties,
title={{SMARTIES}: Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images},
author={Gencer Sumbul and Chang Xu and Emanuele Dalsasso and Devis Tuia},
journal={arXiv preprint arXiv:2506.19585},
year={2025}
}
📄 License
This repository is released under the Apache v2 License.
🙏 Acknowledgements
SMARTIES is supported by the European Space Agency (ESA) through the Discovery and Preparation Program, and is part of the project Toward a Foundation Model for Multi-Sensor Earth Observation Data with Language Semantics.
