CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications
arXiv:2512.15231v3 Announce Type: replace 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 P
The proliferation of massive remote sensing datasets necessitates more advanced, unified, and autonomous processing capabilities, pushing AI research towards agentic systems.
This development in AI agents for remote sensing could significantly enhance Earth observation capabilities, impacting sectors from agriculture and urban planning to defense and climate monitoring.
Current task-specific remote sensing systems could be replaced or augmented by unified, intelligent agents capable of managing diverse, end-to-end workflows autonomously.
- · Earth Observation sector
- · AI software developers
- · Defense and intelligence agencies
- · Environmental monitoring services
- · Legacy remote sensing software providers
- · Manual data processing services
Increased efficiency and accuracy in processing remote sensing data, leading to faster insights.
Expansion of autonomous decision-making in applications reliant on Earth observation, potentially reducing human intervention.
Enhanced geopolitical intelligence and resource management capabilities for nations adopting such advanced AI agents.
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Read at arXiv cs.AI