Multi-Agent Geospatial Copilots for Remote Sensing Workflows

• URL: http://arxiv.org/abs/2501.16254v1
• Date: Mon, 27 Jan 2025 17:54:31 GMT
• Title: Multi-Agent Geospatial Copilots for Remote Sensing Workflows
• Authors: Chaehong Lee, Varatheepan Paramanayakam, Andreas Karatzas, Yanan Jian, Michael Fore, Heming Liao, Fuxun Yu, Ruopu Li, Iraklis Anagnostopoulos, Dimitrios Stamoulis,
• Abstract summary: GeoLLM-Squad introduces the novel multi-agent paradigm to remote sensing (RS)<n>Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving.<n>Our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies.
• Score: 1.8241060496411214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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