MEOX: Compact Multimodal Mixture-of-Experts for Earth Observation
Recent advances in Earth Observation representation learning accommodate heterogeneous sensors and missing observations, often through larger architectures. We present MEOX (Multimodal Earth Observation with eXperts), a multimodal masked autoencoder with a 2.939 million-parameter encoder and 3.115 million parameters in total. Sensor-specific adapters, explicit validity signals, and a shared sparse-expert block preserve modality-dependent processing before a learned patch-wise fusion. Four metadata tokens then accompany a single spatial sequence through fourteen further encoder blocks. Shared e
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- PossiblePossibly related (embedding) · 50%Introducing Gemma 4 12B: a unified, encoder-free multimodal model →
- PossiblePossibly related (embedding) · 48%NeoMME: an efficient Multimodal-native and Multilingual Encoder →
- PossiblePossibly related (embedding) · 46%A unifying framework from neural superposition to sparse interpretable codes →
- LinkedLinked via arxiv author · 85%Mohanad Albughdadi →
“MEOX: Compact Multimodal Mixture-of-Experts for Earth Observation”