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
- Paper: ArXiv
How to Get Started with the Model
Use the code below to get started with the model.
model = AutoModel.from_pretrained("DarthReca/CLOSP-VL", trust_remote_code=True)
Citation
@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 file.