Mixture-of-Experts in Remote Sensing: A Survey

TL;DR

This survey reviews the application of Mixture-of-Experts models in remote sensing, highlighting their principles, architectures, and key tasks, and discusses future research directions.

Contribution

It provides the first comprehensive overview of MoE applications in remote sensing, covering fundamental principles, architectures, and key tasks.

Findings

MoE models effectively handle diverse remote sensing data.

Architectural variations of MoE are tailored for specific remote sensing tasks.

Future trends include integrating MoE with emerging remote sensing technologies.

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.