mlsanityData to 7 Oct 2026

Mixture-of-Experts in Remote Sensing: A Survey

arXiv 2604.03342 ↗First posted 3 Apr 2026cs.CVcited by 0

On the map: Mixture-of-experts → Vision mixture-of-experts

Abstract (arXiv)

Remote sensing data analysis and interpretation present unique challenges due to the diversity in sensor modalities and spatiotemporal dynamics of Earth observation data. Mixture-of-Experts (MoE) model has emerged as a powerful paradigm that addresses these challenges by dynamically routing inputs to specialized experts designed for different aspects of a task. However, despite rapid progress, the community still lacks a comprehensive review of MoE for remote sensing. This survey provides the first systematic overview of MoE applications in remote sensing, covering fundamental principles, architectural designs, and key applications across a variety of remote sensing tasks. The survey also outlines future trends to inspire further research and innovation in applying MoE to remote sensing.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Vision mixture-of-experts, one of 12 directions in Mixture-of-experts. Builds on 91 of its 161 references that are in the map. First posted on arXiv 3 Apr 2026.

Builds on

91 of 161 references.

Closest ancestors · 13

Same direction, newest first

  1. 2025SkyMoE: A Vision-Language Foundation Model for Enhancing Geospatial Interpretation with Mixture of Experts2 Dec
  2. 2025Rethinking Efficient Mixture-of-Experts for Remote Sensing Modality-Missing Classification14 Nov
  3. 2025Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts13 Nov
  4. 2025Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation13 Sep
  5. 2025Generalizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts20 May
  6. 2025MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification29 Apr
  7. 2025RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation4 Apr
  8. 2025Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution12 Feb
  9. 2024RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts7 Dec
  10. 2024Towards Vision Mixture of Experts for Wildlife Monitoring on the Edge12 Nov
  11. 2024RS-MoE: A Vision-Language Model with Mixture of Experts for Remote Sensing Image Captioning and Visual Question Answering3 Nov
  12. 2022M3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design26 Oct
1 more
  1. 2021Scaling Vision with Sparse Mixture of Experts10 Jun

Other references · 78

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  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
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38 more
  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,014
  5. 2024Mixtral of Expertscited by 1,785
  6. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,601
  7. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,283
  8. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  9. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 890
  10. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 834
  11. 2017Remote Sensing Image Scene Classification: Benchmark and State of the Artcited by 791
  12. 2022Mixture-of-Experts with Expert Choice Routingcited by 611
  13. 2022DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scalecited by 400
  14. 2022ST-MoE: Designing Stable and Transferable Sparse Expert Modelscited by 391
  15. 2021BASE Layers: Simplifying Training of Large, Sparse Modelscited by 315
  16. 2024OLMoE: Open Mixture-of-Experts Language Modelscited by 291
  17. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  18. 2021Hash Layers For Large Sparse Modelscited by 236
  19. 2024A Survey on Mixture of Experts in Large Language Modelscited by 233
  20. 2022Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpointscited by 207
  21. 2022MegaBlocks: Efficient Sparse Training with Mixture-of-Expertscited by 204
  22. 2022Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Expertscited by 202
  23. 2022Tutel: Adaptive Mixture-of-Experts at Scalecited by 199
  24. 2024Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Expertscited by 191
  25. 2024Mixture of LoRA Expertscited by 179
  26. 2022Separable Self-attention for Mobile Vision Transformerscited by 163
  27. 2021DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learningcited by 114
  28. 2021Taming Sparsely Activated Transformer with Stochastic Expertscited by 112
  29. 2021FastMoE: A Fast Mixture-of-Expert Training Systemcited by 110
  30. 2023Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inferencecited by 101
  31. 2021Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inferencecited by 96
  32. 2025Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routerscited by 67
  33. 2023Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformerscited by 59
  34. 2023Scaling Expert Language Models with Unsupervised Domain Discoverycited by 59
  35. 2022Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEscited by 58
  36. 2024Vision Foundation Models in Remote Sensing: A Surveycited by 52
  37. 2022Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learnerscited by 51
  38. 2019Convergence Rates for Gaussian Mixtures of Expertscited by 39

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