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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2604.03342 |
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| _version_ | 1866908936251113472 |
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| author | Cui, Yongchuan Liu, Peng Chen, Lajiao |
| author_facet | Cui, Yongchuan Liu, Peng Chen, Lajiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03342 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Mixture-of-Experts in Remote Sensing: A Survey Cui, Yongchuan Liu, Peng Chen, Lajiao Computer Vision and Pattern Recognition 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. |
| title | Mixture-of-Experts in Remote Sensing: A Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.03342 |