Multi-Agent Geospatial Copilots for Remote Sensing Workflows
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
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.
Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 35 of its 62 references that are in the map. First posted on arXiv 27 Jan 2025.
Builds on
35 of 62 references.
Closest ancestors · 2
Same direction, newest first
- 2024An LLM Agent for Automatic Geospatial Data Analysis24 Oct
- 2024Evaluating Tool-Augmented Agents in Remote Sensing Platforms23 Apr
Other references · 33
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Citing papers per year: 2025: 10; 2026: 10.
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- 2026RS-Claw-Evolution: Environment-Feedback-Driven Evolution for Lightweight Remote Sensing Agents in Long-Horizon Taskscited by 0
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- 2026Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operations31 Aug 2026
- 2026GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models11 Jun 2026
- 2026GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis15 Apr 2026
- 2026Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems5 Jan 2026
- 2025CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications17 Dec 2025
- 2025Designing Domain-Specific Agents via Hierarchical Task Abstraction Mechanism21 Nov 2025
- 2025Naiad: Novel Agentic Intelligent Autonomous System for Inland Water Monitoring20 Oct 2025
- 2025CORE: Full-Path Evaluation of LLM Agents Beyond Final State25 Sep 2025
- 2025GeoFlow: Agentic Workflow Automation for Geospatial Tasks5 Aug 2025
- 2025Accelerating Earth Science Discovery via Multi-Agent LLM Systems7 Mar 2025
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- 2025Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challengescited by 1
- 2025REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agentcited by 0
- 2026RingMoClaw: An Experience-Inspired Multi-Agent Framework for Self-Evolving Research in Remote Sensingcited by 0
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- 2026IC-EO: Interpretable Code-based assistant for Earth Observation27 Jan 2026
- 2025REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent21 Nov 2025
- 2025Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges9 Aug 2025
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- 2025Geo-OLM: Enabling Sustainable Earth Observation Studies with Cost-Efficient Open Language Models & State-Driven Workflows6 Apr 2025
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