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README.md

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----
-license: apache-2.0
-tags:
-- earth-observation
-- remote-sensing
-- foundation-model
-- multi-sensor
-pipeline_tag: feature-extraction
----
+<p align="center">
+  <img src="imgs/logo.png" alt="Spectral Coverage" width="400"/>
+</p>
+
+# Spectrum-Aware Multi-Sensor Auto-Encoder for Remote Sensing Images
+[![arXiv](https://img.shields.io/badge/arXiv-2407.02413-b31b1b.svg)](https://arxiv.org/abs/arxiv_id)
+[![HuggingFace](https://img.shields.io/badge/-HuggingFace-3B4252?style=flat&logo=huggingface&logoColor=)](https://huggingface.co/collections/gsumbul/smarties-685888bb5ecded3f802cc945)
+[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+![Python 3.10](https://img.shields.io/badge/Python%203.10-3776AB?logo=python&logoColor=FFF&style=flat)
+![PyTorch 2.6.0](https://img.shields.io/badge/PyTorch-2.6.0-%23ee4c2c?logo=pytorch&logoColor=white)
+[![Website](https://img.shields.io/badge/website-blue?logo=google-chrome)](https://gsumbul.github.io/SMARTIES/)
+
+## 🚀 Introduction
+
+<!-- Spectral coverage figure -->
+<p align="center">
+  <img src="imgs/spectra_fig.png" alt="Spectral Coverage" width="600"/>
+</p>
+
+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.
+
+<!-- Model architecture figure -->
+<p align="center">
+  <img src="imgs/model_fig.png" alt="SMARTIES Model Architecture" width="700"/>
+</p>
+
+This repository contains the model weights of SMARTIES (ViT-B). 
+
+For usage instructions, dataset details, and full documentation, please visit the [SMARTIES GitHub page](https://github.com/gsumbul/SMARTIES). The details of SMARTIES are described in our paper, available on [arXiv](https://arxiv.org/abs/arxiv_id). 
+
+## 📣 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:arxiv_id},
+  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.
+