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arXiv · 2606.25329

State Space Models Meet Remote Sensing: A Survey

Qinzhe Yang·Chenyang Liu·Jia Xu·Zhenwei Shi·Zhengxia Zou
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Abstract

State Space Models (SSMs), designed for long-range modeling, offer linear computational complexity and strong capabilities in capturing long-range dependencies. In the field of remote sensing, SSMs have gained popularity due to their effectiveness in addressing unique challenges such as dense visual predictions, multi-modal remote sensing data, and temporal remote sensing data, which have also yielded significant advancements in customized architectures. This paper presents a comprehensive review of SSM-based approaches in remote sensing, covering most of the relevant studies since SSMs were first introduced to the field. We offer a multi-dimensional analysis examining SSM applications in remote sensing tasks and discussing advancements in architecture design. This paper not only synthesizes the rapid progress in SSM-based research but also identifies key challenges and future opportunities. By providing a detailed perspective, this paper aims to serve as a foundational resource for remote sensing researchers, offering actionable insights to foster further advancements in this evolving domain. We will keep tracing related works at https://github.com/QinzheYang/Awesome-RS-State-Space-Model.

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Qinzhe Yang, Chenyang Liu, Jia Xu, Zhenwei Shi, Zhengxia Zou. 2026-06-24. State Space Models Meet Remote Sensing: A Survey. https://doi.org/10.1007/s11432-025-4780-1

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