Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook

Highlights: What are the main findings? State Space Models (SSMs), especially Mamba, provide linear computational scaling while capturing long-range/global context, making them well-suited to high-resolution remote sensing imagery where CNN receptive fields and ViT quadratic attention become bottlenecks. This paper delivers the first systematic remote sensing-focused survey of Vision Mamba, proposing a holistic taxonomy that covers micro-level innovations (SSM formula refinements, scan strategies, multi-modal/bi-temporal interaction) and macro-level integrations (CNN/Transformer hybrids, framework substitutions, learning paradigms, frequency-domain adaptations), alongside cross-task benchmarking and an outlook on open challenges. What are the implications of the main finding? The taxonomy clarifies the design space of Mamba-based remote sensing models and provides actionable guidance for choosing architectural mechanisms (e.g., scanning and hybridization choices) across key tasks such as detection, segmentation, and change detection. The curated open-source repository and the identified research gaps can accelerate reproducible development, standardized evaluation, and community adoption of SSM-based foundations for next-generation remote sensing analysis. Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements.