AlignJEPA: Predictive Vision-Language Alignment for Remote Sensing Foundation Models
Remote sensing (RS) foundation models provide transferable Earth observation representations across sensors, resolutions, and geographies, yet most remain weakly aligned with natural language, limiting natural-language archive search, image-text retrieval, and question-conditioned analysis. We propose AlignJEPA, a JEPA-inspired predictive vision-language alignment framework for remote sensing foundation models. AlignJEPA uses a pretrained AnySat visual encoder and a RemoteCLIP text encoder while training only a lightweight predictive alignment network. Instead of relying on global image--text contrastive alignment alone, the framework predicts remote-sensing text embeddings from masked vi...
| Authors | Md Aminur Hossain, Omkumar Vaghasiya, Rajeev Ranjan Dwivedi et al. |
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| Primary category | cs.CV |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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