RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation

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RingMoE significantly improves change detection and detection accuracy across multiple remote sensing benchmarks.
The model employs 14.7 billion parameters and leverages a hierarchical mixture-of-experts architecture.
Assessment included 23 benchmarks across tasks such as segmentation and classification, displaying superior performance.
The findings suggest RingMoE may enable more effective use of multi-spectral and synthetic aperture radar data.

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Observational analysis demonstrates improved classification and segmentation in remote sensing, indicating RingMoE's adaptability across modalities.

Bi et al. (2025) studied this question.

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Observational analysis demonstrates improved classification and segmentation in remote sensing, indicating RingMoE's adaptability across modalities.