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Mixture-of-Experts in Remote Sensing: A Survey
Review Article  ·  Published: 23 March 2026
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Journal of Geoscience and Remote Sensing
Volume 1, Issue 1, 2025: 4-38
Review Article Open Access

Mixture-of-Experts in Remote Sensing: A Survey

by
Yongchuan Cui Yongchuan Cui 1,2 ORCID
Peng Liu Peng Liu 1,2 * ORCID
Lajiao Chen Lajiao Chen 1,2
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2 School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
Corresponding Author: Peng Liu, [email protected]
Volume 1, Issue 1
Volume 1, Issue 1 2025
Received: 17 December 2025 Accepted: 13 January 2026 Published: 23 March 2026 CrossMark
Download PDF 3.34 MB Cite Metrics
DOI: 10.62762/JGRS.2025.140654
ARK: ark:/57805/jgrs.2025.140654

Article Information

Published in Journal of Geoscience and Remote Sensing
Volume/Issue Volume 1, Issue 1, 2025
Pages 4-38

Abstract

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.

Graphical Abstract

Mixture-of-Experts in Remote Sensing: A Survey

Keywords

Mixture-of-Experts remote sensing image classification vision-language models object detection change detection multi-modal fusion super-resolution

Data Availability Statement

Not applicable.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant U2243222 and Grant 61731022, in part by the National Key Research and Development Program of China under Grant 2024YFF1307204, and in part by the Project of Comprehensive Site Selection System under Grant KY24004.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Cui, Y., Liu, P., & Chen, L. (2026). Mixture-of-Experts in Remote Sensing: A Survey. Journal of Geoscience and Remote Sensing, 1(1), 4–38. https://doi.org/10.62762/JGRS.2025.140654
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TY  - JOUR
AU  - Cui, Yongchuan
AU  - Liu, Peng
AU  - Chen, Lajiao
PY  - 2026
DA  - 2026/03/23
TI  - Mixture-of-Experts in Remote Sensing: A Survey
JO  - Journal of Geoscience and Remote Sensing
T2  - Journal of Geoscience and Remote Sensing
JF  - Journal of Geoscience and Remote Sensing
VL  - 1
IS  - 1
SP  - 4
EP  - 38
DO  - 10.62762/JGRS.2025.140654
UR  - https://www.icck.org/article/abs/JGRS.2025.140654
KW  - Mixture-of-Experts
KW  - remote sensing
KW  - image classification
KW  - vision-language models
KW  - object detection
KW  - change detection
KW  - multi-modal fusion
KW  - super-resolution
AB  - 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.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Cui2026MixtureofE,
  author = {Yongchuan Cui and Peng Liu and Lajiao Chen},
  title = {Mixture-of-Experts in Remote Sensing: A Survey},
  journal = {Journal of Geoscience and Remote Sensing},
  year = {2026},
  volume = {1},
  number = {1},
  pages = {4-38},
  doi = {10.62762/JGRS.2025.140654},
  url = {https://www.icck.org/article/abs/JGRS.2025.140654},
  abstract = {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.},
  keywords = {Mixture-of-Experts, remote sensing, image classification, vision-language models, object detection, change detection, multi-modal fusion, super-resolution},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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