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.
and Sun, M
3 Pith papers cite this work. Polarity classification is still indexing.
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PDFM embeddings reduce unexplained variance in subnational population estimates by a median 20.1% versus geospatial covariates, with gains strongest in larger less-developed areas but weaker transfer across scales.
Instability-guided perturbations in the Aurora AI model can induce downstream shifts in an atmospheric river's moisture transport, potentially lowering landfall intensity in a California case study.
citing papers explorer
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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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Geospatial foundation-model embeddings improve population estimation unevenly across space and scale
PDFM embeddings reduce unexplained variance in subnational population estimates by a median 20.1% versus geospatial covariates, with gains strongest in larger less-developed areas but weaker transfer across scales.
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Instability-Aware Steering of an Extreme Atmospheric River in an AI Weather Foundation Model
Instability-guided perturbations in the Aurora AI model can induce downstream shifts in an atmospheric river's moisture transport, potentially lowering landfall intensity in a California case study.