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--- |
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license: openrail |
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datasets: |
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- DarthReca/crisislandmark |
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language: |
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- en |
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library_name: transformers |
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tags: |
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- remote-sensing |
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- text-to-image-retrieval |
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- multimodal |
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- geospatial |
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- SAR |
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- multispectral |
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- crisis-management |
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- earth-observation |
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- contrastive-learning |
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base_model: |
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- sentence-transformers/all-MiniLM-L6-v2 |
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--- |
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# CLOSP-VL |
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CLOSP (Contrastive Language Optical SAR Pretraining) is a multimodal architecture designed for text-to-image retrieval. |
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It creates a unified embedding space for text, Sentinel-2 (MSI), and Sentinel-1 (SAR) data. |
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The CLOSP-VL variant uses a ViT-large vision backbone. |
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## Model Details |
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The model uses three separate encoders: one for text, one for Sentinel-1 (SAR) data, and one for Sentinel-2 (MSI) data. |
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During training, it uses a contrastive objective to align the textual embeddings with the corresponding visual embeddings (either SAR or MSI). |
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- **Developed by:** Daniele Rege Cambrin |
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- **Model type:** CLOSP |
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- **Language(s) (NLP):** english |
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- **License:** OpenRAIL |
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- **Finetuned from model:** [More Information Needed] |
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- **Repository:** [GitHub](https://github.com/DarthReca/closp) |
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- **Paper:** [ArXiv](https://arxiv.org/abs/2507.10403) |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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model = AutoModel.from_pretrained("DarthReca/CLOSP-VL", trust_remote_code=True) |
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``` |
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## Citation |
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```bibtex |
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@misc{cambrin2025texttoremotesensingimageretrievalrgbsources, |
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title={Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources}, |
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author={Daniele Rege Cambrin and Lorenzo Vaiani and Giuseppe Gallipoli and Luca Cagliero and Paolo Garza}, |
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year={2025}, |
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eprint={2507.10403}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2507.10403}, |
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} |
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``` |
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## Licensing |
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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. |
