CLOSP-Visual / README.md
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---
license: openrail
datasets:
- DarthReca/crisislandmark
language:
- en
library_name: torchgeo
tags:
- remote-sensing
- text-to-image-retrieval
- multimodal
- geospatial
- SAR
- multispectral
- crisis-management
- earth-observation
- contrastive-learning
---
# CLOSP
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.
This repository contains all the separate visual encoders in PyTorch format.
## 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
- **Repository:** [GitHub](https://github.com/DarthReca/closp)
- **Paper:** [ArXiv](https://arxiv.org/abs/2507.10403)
## 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},
}
```