RS-AGENT: Large Language Models Guided Agent System for Remote Sensing Image Generation

Remote Sensing Image Generation (RSIG) offers a viable solution to the high data collection costs by facilitating the generation of large datasets. However, it is often hindered by the complexity of tasks and the diverse requirements of generated images. This paper presents the RS-Agent system, a novel approach that harnesses the capabilities of Large Language Models (LLMs) within an innovative agent solution, effectively addressing these issues. The system comprises a Task Agent, a Prompt Agent, and a LoRA Agent, each performing crucial roles in task decomposition, prompt generation, and scene-specific fine-tuning, respectively. Anchored by Diffusion models and employing natural language dialogues for interaction, the system aligns closely with user intent and produces high-quality results. Experimental results demonstrate the efficacy of the RS-Agent system in managing diverse RSIG tasks, adapting to various input forms, and generating high-quality results.