Mixture-of-Experts in Remote Sensing: A Survey

Yongchuan Cui; Peng Liu; Lajiao Chen

cs.CVApr 3, 2026

Mixture-of-Experts in Remote Sensing: A Survey

Authors: Yongchuan Cui, Peng Liu, Lajiao Chen

Organizations: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China

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.

Figures & tables

Figure 1: Word cloud of the most frequent words appearing in MoE-related remote sensing papers.

Figure 2: Basic architecture of Mixture-of-Experts (MoE).

Panel of Figure 3 — Figure 3: Overview of Mixture-of-Experts applications in remote sensing.

Mixture-of-Experts Models

Mixture-of-experts models use input-dependent routing to activate specialized subnetworks, increasing model capacity without running every parameter for each input.

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Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey

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Mixture-of-Experts (MoE) presents a naturally compatible and scalable framework for multimodal learning, demonstrating strong adaptability across diverse modalities and tasks. Despite its growing success, a comprehensive and systematic review on the MoE metho addressing multimodal challenges remains lacking. Existing surveys tend to evaluate either multimodal learning or MoE independently from method taxonomy, overlooking the unique interplay between them. This survey fills that gap by answering a central question: How does MoE effectively resolve multimodal challenges? We approach this from three key perspectives: (1) MoE as an Efficient Multimodal Engine: enabling scalable multimodal modeling by decoupling computational cost from parameter growth and mitigating modality redundancy through selective expert activation; (2) MoE as a Multimodal Representation Learner: integrating complementary multi-opinion expert knowledge to enrich alignment and interaction representations; and (3) MoE as a Multimodal Adapter: providing a modular and flexible mechanism to model imperfect data scenarios such as modality imbalance and missing modality. Through our extensive literature review, we identify critical research gaps, including interpretable routing, expert communication, modality integration, and lifelong multimodal learning. We position this survey as a foundation for future research toward interpretable and sustainable multimodal Mixture-of-Experts system.

Abstract

Mixture-of-Experts Models

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