The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
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A visualization protocol based on unsupervised semantic segmentation reveals positional biases, scaling behaviors, and boundary artifacts across self-supervised vision transformer models.
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Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting limitations in grounding and safety.
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Unsupervised Semantic Segmentation Facilitates Model Understanding
A visualization protocol based on unsupervised semantic segmentation reveals positional biases, scaling behaviors, and boundary artifacts across self-supervised vision transformer models.