Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems

2026-01-05

arXiv.org

AI Summary

This work addresses the limitations of conventional remote sensing analysis, which relies on static deep learning models and struggles to support the sequential planning and active tool coordination required for complex geospatial tasks. The study introduces the first unified taxonomy for remote sensing agents, encompassing both single-agent collaborators and multi-agent systems, and systematically examines core components such as planning mechanisms, retrieval-augmented generation, and memory architectures. It advocates a paradigm shift in evaluation—from pixel-level accuracy to trajectory-aware reasoning correctness—and establishes foundational theory alongside a new benchmark. The paper further identifies critical challenges in grounding, safety, and orchestration capabilities, and outlines a roadmap toward robust, general-purpose geospatial intelligence.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Agentic AI
• Remote Sensing
• Geospatial Intelligence
• Autonomous Systems
• Sequential Planning

Innovation

• Agentic AI
• remote sensing
• multi-agent systems
• retrieval-augmented generation
• trajectory-aware reasoning