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-RN variant uses a ResNet-50 vision backbone.
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).
Use the code below to get started with the model.
1@misc{cambrin2025texttoremotesensingimageretrievalrgbsources,
2 title={Text-to-Remote-Sensing-Image Retrieval beyond RGB Sources},
3 author={Daniele Rege Cambrin and Lorenzo Vaiani and Giuseppe Gallipoli and Luca Cagliero and Paolo Garza},
4 year={2025},
5 eprint={2507.10403},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2507.10403},
9}
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.
