RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Remote Sensing · Depth: Expert, extended

Summary

RS-Agent is a novel LLM-driven intelligent agent designed to automate complex remote sensing tasks, overcoming limitations of existing models in specialized domains. It functions with a Large Language Model (LLM) as its "Central Controller" for understanding user intentions and integrates numerous high-performance remote sensing image processing tools for multi-tool, multi-turn conversations. The agent also incorporates Retrieval-Augmented Generation (RAG) to access a specialized knowledge database, enabling it to answer professional questions. Experiments on datasets like RSSDIVCS, RSVQA, and DOTAv1 demonstrate RS-Agent's superior performance in scene classification (e.g., 98.00% accuracy on RSSDIVCS), visual question answering, and object counting (33.30% absolute accuracy on DOTAv1) compared to other VLM models.

Key takeaway

For AI Engineers developing remote sensing applications, RS-Agent offers a robust framework to enhance automation and domain-specific intelligence. You should consider integrating LLM-driven agents with specialized toolchains and RAG for complex tasks like object counting or VQA, reducing reliance on manual expert intervention. This approach improves accuracy and enables multi-turn interactions, streamlining your analytical workflows.

Key insights

RS-Agent leverages LLMs, integrated tools, and RAG to automate complex remote sensing image analysis and specialized knowledge queries.

Principles

• LLMs act as central controllers for task planning and intent comprehension.
• Integrate diverse tools for complex, multi-step problem-solving.
• Utilize RAG for domain-specific knowledge and solution guidance.

Method

The RS-Agent workflow involves an LLM (gpt-3.5-turbo) as a Central Controller, which uses a Solution Searcher (FAISS-based RAG) to select tools and a Knowledge Searcher (RAG) for domain-specific information, then executes selected tools.

In practice

• Automate scene classification with high accuracy on datasets like RSSDIVCS.
• Perform visual question answering on remote sensing images.
• Conduct precise object counting using models like YOLOv8x-OBB.

Topics

• Remote Sensing Agents
• Large Language Models
• Retrieval-Augmented Generation
• Visual Question Answering
• Object Counting
• Scene Classification

Code references

• hwchase17/langchain
• ultralytics/yolov8

Best for: Computer Vision Engineer, AI Scientist, AI Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.