CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications
🤖 AI Summary
Existing remote sensing intelligent processing systems are predominantly task-specific, lacking a unified framework capable of supporting end-to-end multi-task workflows, thereby hindering adaptability to diverse application requirements. To address this, we propose the first unified agent framework tailored for remote sensing intelligence, integrating expert-validated knowledge (1,008 real-world workflows) with a dynamically evolvable workflow mechanism. Our framework features a Programmable Knowledge Base (PKB), a dynamic adaptation module, and an evolutionary memory system—collectively enabling hallucination suppression, autonomous failure recovery, and continuous capability evolution. It supports 13 mainstream large language model backbones and achieves ≥4% higher task success rate than Reflexion on our newly constructed benchmark, KnowFlow-Bench (324 real-world scenarios). This constitutes the most comprehensive empirical validation of a remote sensing intelligent agent to date.
Application Category
📝 Abstract
The automated and intelligent processing of massive remote sensing (RS) datasets is critical in Earth observation (EO). Existing automated systems are normally task-specific, lacking a unified framework to manage diverse, end-to-end workflows--from data preprocessing to advanced interpretation--across diverse RS applications. To address this gap, this paper introduces CangLing-KnowFlow, a unified intelligent agent framework that integrates a Procedural Knowledge Base (PKB), Dynamic Workflow Adjustment, and an Evolutionary Memory Module. The PKB, comprising 1,008 expert-validated workflow cases across 162 practical RS tasks, guides planning and substantially reduces hallucinations common in general-purpose agents. During runtime failures, the Dynamic Workflow Adjustment autonomously diagnoses and replans recovery strategies, while the Evolutionary Memory Module continuously learns from these events, iteratively enhancing the agent's knowledge and performance. This synergy enables CangLing-KnowFlow to adapt, learn, and operate reliably across diverse, complex tasks. We evaluated CangLing-KnowFlow on the KnowFlow-Bench, a novel benchmark of 324 workflows inspired by real-world applications, testing its performance across 13 top Large Language Model (LLM) backbones, from open-source to commercial. Across all complex tasks, CangLing-KnowFlow surpassed the Reflexion baseline by at least 4% in Task Success Rate. As the first most comprehensive validation along this emerging field, this research demonstrates the great potential of CangLing-KnowFlow as a robust, efficient, and scalable automated solution for complex EO challenges by leveraging expert knowledge (Knowledge) into adaptive and verifiable procedures (Flow).
Problem
Research questions and friction points this paper is trying to address.
Addresses lack of unified framework for diverse remote sensing workflows
Integrates expert knowledge to reduce hallucinations in automated agents
Enables autonomous diagnosis and learning for complex Earth observation tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates Procedural Knowledge Base with expert-validated workflow cases
Uses Dynamic Workflow Adjustment to autonomously diagnose and replan
Employs Evolutionary Memory Module to continuously learn and enhance performance
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