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.
An LLM agent for automatic geospatial data analy- sis
5 Pith papers cite this work. Polarity classification is still indexing.
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SAGA is a schema-grounded agent framework that extracts facts, validates schemas, plans augmentation strategies, and evaluates generated SAR samples for quality and downstream utility.
Medoid prototype alignment detects unknown attacks across industrial plants by aligning domain-specific medoid summaries rather than raw samples, yielding 0.843 average accuracy on gas and water system transfers.
CMIP-Forge presents a retrieval-augmented agentic system with automated guardrails and adversarial self-review for autonomous execution of climate research tasks on CMIP6 literature and ESGF data.
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.
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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A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation
SAGA is a schema-grounded agent framework that extracts facts, validates schemas, plans augmentation strategies, and evaluates generated SAR samples for quality and downstream utility.
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Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systems
Medoid prototype alignment detects unknown attacks across industrial plants by aligning domain-specific medoid summaries rather than raw samples, yielding 0.843 average accuracy on gas and water system transfers.
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CMIP-Forge: An Agentic System that Retrieves, Computes, and Self-Reviews Climate Science
CMIP-Forge presents a retrieval-augmented agentic system with automated guardrails and adversarial self-review for autonomous execution of climate research tasks on CMIP6 literature and ESGF data.
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UniReason-Med: A Shared Grounded Reasoning Interface for 2D-to-3D Transfer in Medical VQA
UniReason-Med introduces a unified framework for 2D and 3D medical VQA with shared grounded reasoning, trained on a 220K dataset, claiming that joint 2D+3D supervision improves 3D performance over 3D-only training.