多智能体架构提升遥感工作流效率，性能优于单一模型17%
Multi-Agent Geospatial Copilots for Remote Sensing Workflows
- 将遥感任务拆分给专业子智能体，分工协作提升效率
- 在复杂任务下表现更稳，较顶尖基线提升17%正确率
- 适合城市监测、林业保护等多领域遥感应用
我们提出GeoLLM-Squad，一种面向遥感工作流的地理空间协作者，首次引入多智能体范式。与依赖统一大语言模型的单智能体方法不同，GeoLLM-Squad将智能体编排与地理空间任务求解分离，将遥感任务委派给专用子智能体。基于开源AutoGen和GeoLLM-Engine框架，该系统可模块化集成城市监测、林业保护、气候分析和农业研究等多种应用。结果表明，单智能体系统在任务复杂度增加时表现下降，而GeoLLM-Squad保持稳健性能，相比现有最优基线在智能体正确性上提升17%。研究验证了多智能体AI在推进遥感工作流中的潜力。
原文摘要 · Abstract (English)
We present GeoLLM-Squad, a geospatial Copilot that introduces the novel multi-agent paradigm to remote sensing (RS) workflows. Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving, by delegating RS tasks to specialized sub-agents. Built on the open-source AutoGen and GeoLLM-Engine frameworks, our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies. Our results demonstrate that while single-agent systems struggle to scale with increasing RS task complexity, GeoLLM-Squad maintains robust performance, achieving a 17% improvement in agentic correctness over state-of-the-art baselines. Our findings highlight the potential of multi-agent AI in advancing RS workflows.
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