Agentic AI 在遥感中的应用：技术挑战与研究方向
Agentic AI for Remote Sensing: Technical Challenges and Research Directions
AI总结：
本文指出遥感中的多步分析工作流存在结构性的地理空间约束，提出面向地球观测的原生智能体设计原则，包括结构化地理空间状态、工具感知推理、验证器引导执行和有效性感知学习评估。
摘要：
地球观测（EO）正从静态预测转向多步分析工作流，这些工作流需要对数据、工具和地理空间状态进行协调推理。尽管基础模型和视觉语言模型在遥感中推进了表示学习和语言基础交互，并且智能体AI在长期推理和工具使用方面显示出强大潜力，但EO并非通用智能体AI的直接扩展。EO工作流处理的是地理参考、多模态和时间结构化的数据，其中重投影、重采样、合成和聚合等操作会改变底层状态，并可能限制后续分析。因此，错误可能跨步骤无声传播，正确性不仅取决于内部一致性，还取决于地理空间一致性、时间有效比较和物理有效性。本文立场是这些挑战是结构性的而非偶然的。我们审视了通用智能体系统中常见的假设，分析了它们在地理空间工作流中失效的方式，并描述了多步EO流水线中的故障模式。然后，我们概述了面向EO的原生智能体设计原则，这些原则围绕结构化地理空间状态、工具感知推理、验证器引导执行以及有效性感知学习和评估。因此，构建可靠的地理空间智能体需要围绕控制EO分析的物理、地理空间和工作流约束重新思考智能体设计。
英文摘要：
Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, and geospatial state. While foundation models and vision-language models have advanced representation learning and language-grounded interaction in remote sensing, and agentic AI has shown strong potential for long-horizon reasoning and tool use, EO is not a straightforward extension of generic agentic AI. EO workflows operate on georeferenced, multi-modal, and temporally structured data, where operations such as reprojection, resampling, compositing, and aggregation transform the underlying state and can constrain later analysis. As a result, errors may propagate silently across steps, and correctness depends not only on internal coherence but also on geospatial consistency, temporally valid comparisons, and physical validity. This position paper argues that these challenges are structural rather than incidental. We examine the assumptions commonly made in generic agentic systems, analyze how they break in geospatial workflows, and characterize failure modes in multi-step EO pipelines. We then outline design principles for EO-native agents centered on structured geospatial state, tool-aware reasoning, verifier-guided execution, and validity-aware learning and evaluation. Building reliable geospatial agents, therefore, requires rethinking agent design around the physical, geospatial, and workflow constraints that govern EO analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
