An increasing number of models have achieved great performance in remote sensing tasks with the recent development of Large Language Models (LLMs) and Visual Language Models (VLMs). However, these models are constrained to basic vision and language instruction-tuning tasks, facing challenges in complex remote sensing applications. Additionally, these models lack specialized expertise in professional domains. To address these limitations, we propose a LLM-driven remote sensing intelligent agent named RS-Agent. Firstly, RS-Agent is powered by a large language model (LLM) that acts as its "Central Controller," enabling it to understand and respond to various problems intelligently. Secondly, our RS-Agent integrates many high-performance remote sensing image processing tools, facilitating multi-tool and multi-turn conversations. Thirdly, our RS-Agent can answer professional questions by leveraging robust knowledge documents. We conducted experiments using several datasets, e.g., RSSDIVCS, RSVQA, and DOTAv1. The experimental results demonstrate that our RS-Agent delivers outstanding performance in many tasks, i.e., scene classification, visual question answering, and object counting tasks.

提出使用大型语言模型（LLM）驱动的遥感智能代理RS-Agent来解决遥感应用中的复杂问题，通过整合高性能的遥感图像处理工具和利用有力的知识文档回答专业问题，实验结果表明RS-Agent在场景分类等任务中具有出色性能。

RS-Agent: 智能代理自动化遥感任务