arXiv:2606.25329v1 Announce Type: cross 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, cover
The proliferation of remote sensing data and the increasing maturity of State Space Models provide a timely intersection for this survey, indicating growing practical application and research focus.
This survey highlights a critical advancement in AI's ability to process and understand vast, complex geospatial data, which has significant implications for environmental monitoring, urban planning, and defense applications.
The explicit application and architectural customization of SSMs for remote sensing data suggest more efficient and capable AI systems for analyzing Earth observation information, moving beyond traditional computer vision approaches.
- · Remote Sensing Data Providers
- · GIS Software Developers
- · Environmental Monitoring Agencies
- · Defense and Intelligence Sectors
- · Legacy Remote Sensing Analysis Techniques
- · Ad-hoc Computer Vision Models without Long-Range Modeling
Improved accuracy and efficiency in satellite imagery analysis for various applications.
Enhanced capabilities for predictive modeling of environmental changes and resource management.
Potential for new autonomous systems that make decisions based on real-time, comprehensive geospatial intelligence.
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