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arxiv: 2606.25329 · v1 · pith:RTS6M5ZSnew · submitted 2026-06-24 · 💻 cs.CV · cs.LG

State Space Models Meet Remote Sensing: A Survey

Qinzhe Yang , Chenyang Liu , Jia Xu , Zhenwei Shi , Zhengxia Zou This is my paper

Pith reviewed 2026-06-25 21:15 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords state space modelsremote sensingsurveylong-range dependenciesmulti-modal datatemporal dataarchitecture design
0
0 comments X

The pith

State space models are reviewed for their applications in remote sensing tasks with linear complexity for long-range dependencies.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper compiles a comprehensive review of state space model approaches applied to remote sensing since their introduction to the field. It examines their use across tasks involving dense visual predictions, multi-modal data, and temporal sequences, while also covering custom architecture designs that have emerged. A sympathetic reader would care because the models offer an efficient way to handle long-range dependencies that arise in remote sensing data. The survey synthesizes recent progress and flags key challenges along with future opportunities to guide additional research in the area.

Core claim

The 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. It offers 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, aiming to serve as a foundational resource for remote sensing researchers and offer actionable insights to foster further advancements.

What carries the argument

State Space Models (SSMs) for long-range modeling, which deliver linear computational complexity while capturing long-range dependencies in remote sensing data.

Load-bearing premise

The authors have successfully identified and included essentially all relevant SSM papers in remote sensing since the models' introduction to the domain.

What would settle it

Discovery of one or more significant published papers applying state space models to remote sensing that are omitted from both the survey text and the linked GitHub repository would falsify the coverage claim.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper claims to deliver a comprehensive survey of State Space Models (SSMs) in remote sensing, covering most relevant studies since their introduction to the field. It provides a multi-dimensional analysis of SSM applications across remote sensing tasks (dense visual predictions, multi-modal data, temporal data) and architecture advancements, synthesizes progress, identifies challenges and future opportunities, and maintains an updated GitHub repository of works.

Significance. If the coverage claim holds, the survey would be a useful foundational resource for remote sensing researchers by organizing the rapid growth of SSM adaptations and offering actionable insights on architecture design. The commitment to an ongoing GitHub list (https://github.com/QinzheYang/Awesome-RS-State-Space-Model) is a concrete strength that supports currency and reproducibility of the review.

major comments (1)
  1. [Abstract and Introduction] Abstract and Introduction: The central claim that the paper presents a 'comprehensive review' covering 'most of the relevant studies' is not supported by any description of the literature search methodology. No search strings, databases (e.g., arXiv, IEEE Xplore, Google Scholar), date ranges, inclusion/exclusion criteria, or PRISMA-style flow diagram are provided. This directly undermines evaluation of the breadth and accuracy assertions that define the survey's value.
minor comments (2)
  1. The multi-dimensional analysis structure (tasks vs. architectures) is mentioned but would benefit from an explicit taxonomy diagram or table early in the paper to help readers navigate the review.
  2. The GitHub repository is referenced but its scope, update frequency, and relationship to the paper's included studies are not described in the text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment and for recognizing the potential value of the survey. We agree that methodological transparency is important and will revise the manuscript to address this concern.

read point-by-point responses

  1. Referee: [Abstract and Introduction] Abstract and Introduction: The central claim that the paper presents a 'comprehensive review' covering 'most of the relevant studies' is not supported by any description of the literature search methodology. No search strings, databases (e.g., arXiv, IEEE Xplore, Google Scholar), date ranges, inclusion/exclusion criteria, or PRISMA-style flow diagram are provided. This directly undermines evaluation of the breadth and accuracy assertions that define the survey's value.

    Authors: We agree that a clear description of the literature search process is necessary to support claims of comprehensiveness. The original manuscript did not include such details. In the revised version we will add a dedicated subsection (likely in the Introduction) that specifies the databases searched (Google Scholar, arXiv, IEEE Xplore), the search strings and keywords employed, the date range considered, inclusion/exclusion criteria, and the overall selection workflow. We will also include a PRISMA-style flow diagram or equivalent summary of paper counts at each stage. This addition will directly address the referee's concern and strengthen the survey's credibility. revision: yes

Circularity Check

0 steps flagged

No circularity: survey paper contains no derivations or fitted quantities

full rationale

This is a literature review surveying SSM applications in remote sensing. It contains no equations, no parameter fitting, no predictions derived from inputs, and no derivation chain of any kind. All enumerated circularity patterns require the presence of such mathematical or predictive steps that reduce to self-definition or self-citation; none are present. The completeness claim about coverage of papers is an empirical assertion about literature search (not a derivation), and the GitHub link is external. The work is therefore self-contained with score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper. No free parameters, axioms, or invented entities are introduced because no technical derivation or model is proposed.

pith-pipeline@v0.9.1-grok · 5712 in / 935 out tokens · 19164 ms · 2026-06-25T21:15:45.401960+00:00 · methodology

discussion (0)

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Reference graph

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