README.md

---
license: openrail
datasets:
- DarthReca/crisislandmark
language:
- en
library_name: transformers
tags:
- remote-sensing
- text-to-image-retrieval
- multimodal
- geospatial
- SAR
- multispectral
- crisis-management
- earth-observation
- contrastive-learning
base_model:
- sentence-transformers/all-MiniLM-L6-v2
---
# CLOSP-VL

CLOSP (Contrastive Language Optical SAR Pretraining) is a multimodal architecture designed for text-to-image retrieval. 
It creates a unified embedding space for text, Sentinel-2 (MSI), and Sentinel-1 (SAR) data. 
The CLOSP-VL variant uses a ViT-large vision backbone.

## Model Details
The model uses three separate encoders: one for text, one for Sentinel-1 (SAR) data, and one for Sentinel-2 (MSI) data. 
During training, it uses a contrastive objective to align the textual embeddings with the corresponding visual embeddings (either SAR or MSI).


- **Developed by:** Daniele Rege Cambrin
- **Model type:** CLOSP
- **Language(s) (NLP):** english
- **License:** OpenRAIL
- **Finetuned from model:** [More Information Needed]
- **Repository:** [GitHub](https://github.com/DarthReca/closp)
- **Paper:** [ArXiv](https://arxiv.org/abs/2507.10403)

## How to Get Started with the Model

Use the code below to get started with the model.

```python
model = AutoModel.from_pretrained("DarthReca/CLOSP-VL", trust_remote_code=True)  
```

## Citation

```bibtex
@misc{cambrin2025texttoremotesensingimageretrievalrgbsources,
      title={Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources}, 
      author={Daniele Rege Cambrin and Lorenzo Vaiani and Giuseppe Gallipoli and Luca Cagliero and Paolo Garza},
      year={2025},
      eprint={2507.10403},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.10403}, 
}
```

## Licensing
The data in this dataset is a compilation of multiple sources, each with its own license. For detailed information on the licensing of each component, please see the [**NOTICE.md**](NOTICE.md) file.