Update README.md

README.md

@@ -1,3 +1,60 @@
----
-license: openrail
----
+---
+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}, 
+}
+```