让遥感分析从被动识别转向主动决策的智能体系统
Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
- 构建遥感智能体的统一分类框架，区分单智能体与多智能体系统
- 提出基于规划与记忆的架构，支持复杂地理任务的自主执行
- 推动评估从像素精度转向轨迹推理正确性，适合遥感与AI交叉研究者
地球观测分析正从静态深度学习模型转向自主智能体AI。尽管近期视觉基础模型和多模态大语言模型提升了表征学习能力，但通常缺乏复杂地理工作流所需的序列规划与主动工具调度能力。本文首次全面综述遥感领域的智能体AI，提出统一分类体系，区分单智能体协作助手与多智能体系统，并分析规划机制、检索增强生成与记忆结构等架构基础。同时，梳理新兴基准测试，将评估范式从像素级准确率转向轨迹感知的推理正确性。通过批判性分析在定位、安全与编排方面的局限，本文勾勒出构建稳健自主地理情报系统的战略路线图。
原文摘要 · Abstract (English)
The paradigm of Earth Observation analysis is shifting from static deep learning models to autonomous agentic AI. Although recent vision foundation models and multimodal large language models advance representation learning, they often lack the sequential planning and active tool orchestration required for complex geospatial workflows. This survey presents the first comprehensive review of agentic AI in remote sensing. We introduce a unified taxonomy distinguishing between single-agent copilots and multi-agent systems while analyzing architectural foundations such as planning mechanisms, retrieval-augmented generation, and memory structures. Furthermore, we review emerging benchmarks that move the evaluation from pixel-level accuracy to trajectory-aware reasoning correctness. By critically examining limitations in grounding, safety, and orchestration, this work outlines a strategic roadmap for the development of robust, autonomous geospatial intelligence.
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