  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.
