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RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

Wenjia Xu, Zijian Yu, Boyang Mu, Zhiwei Wei, Yuanben Zhang, Guangzuo Li, Mugen Peng

TL;DR

RS-Agent addresses the limitations of general multimodal LLMs in remote sensing by introducing an autonomous agent that orchestrates specialized models through four components: Central Controller, Toolkit, Solution Space, and Knowledge Space. It introduces Task-Aware Retrieval to guide task planning and DualRAG to enhance domain knowledge retrieval via weighted, dual-path retrieval. Across 9 datasets and 18 remote sensing tasks, RS-Agent achieves over 95% task planning accuracy and outperforms baselines in scene classification, object counting, and RSVQA, while remaining compatible with both open-source and proprietary LLMs. The framework is modular and extensible, delivering final outputs $A$ and processed image $\hat{I}$ and offering a scalable path toward automated, expert-level remote sensing analysis.

Abstract

The unprecedented advancements in Multimodal Large Language Models (MLLMs) have demonstrated strong potential in interacting with humans through both language and visual inputs to perform downstream tasks such as visual question answering and scene understanding. However, these models are constrained to basic instruction-following or descriptive tasks, facing challenges in complex real-world remote sensing applications that require specialized tools and knowledge. To address these limitations, we propose RS-Agent, an AI agent designed to interact with human users and autonomously leverage specialized models to address the demands of real-world remote sensing applications. RS-Agent integrates four key components: a Central Controller based on large language models, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain-level reasoning, enabling it to interpret user queries and orchestrate tools for accurate remote sensing task. We introduce two novel mechanisms: Task-Aware Retrieval, which improves tool selection accuracy through expert-guided planning, and DualRAG, a retrieval-augmented generation method that enhances knowledge relevance through weighted, dual-path retrieval. RS-Agent supports flexible integration of new tools and is compatible with both open-source and proprietary LLMs. Extensive experiments across 9 datasets and 18 remote sensing tasks demonstrate that RS-Agent significantly outperforms state-of-the-art MLLMs, achieving over 95% task planning accuracy and delivering superior performance in tasks such as scene classification, object counting, and remote sensing visual question answering. Our work presents RS-Agent as a robust and extensible framework for advancing intelligent automation in remote sensing analysis.

RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

TL;DR

RS-Agent addresses the limitations of general multimodal LLMs in remote sensing by introducing an autonomous agent that orchestrates specialized models through four components: Central Controller, Toolkit, Solution Space, and Knowledge Space. It introduces Task-Aware Retrieval to guide task planning and DualRAG to enhance domain knowledge retrieval via weighted, dual-path retrieval. Across 9 datasets and 18 remote sensing tasks, RS-Agent achieves over 95% task planning accuracy and outperforms baselines in scene classification, object counting, and RSVQA, while remaining compatible with both open-source and proprietary LLMs. The framework is modular and extensible, delivering final outputs and processed image and offering a scalable path toward automated, expert-level remote sensing analysis.

Abstract

The unprecedented advancements in Multimodal Large Language Models (MLLMs) have demonstrated strong potential in interacting with humans through both language and visual inputs to perform downstream tasks such as visual question answering and scene understanding. However, these models are constrained to basic instruction-following or descriptive tasks, facing challenges in complex real-world remote sensing applications that require specialized tools and knowledge. To address these limitations, we propose RS-Agent, an AI agent designed to interact with human users and autonomously leverage specialized models to address the demands of real-world remote sensing applications. RS-Agent integrates four key components: a Central Controller based on large language models, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain-level reasoning, enabling it to interpret user queries and orchestrate tools for accurate remote sensing task. We introduce two novel mechanisms: Task-Aware Retrieval, which improves tool selection accuracy through expert-guided planning, and DualRAG, a retrieval-augmented generation method that enhances knowledge relevance through weighted, dual-path retrieval. RS-Agent supports flexible integration of new tools and is compatible with both open-source and proprietary LLMs. Extensive experiments across 9 datasets and 18 remote sensing tasks demonstrate that RS-Agent significantly outperforms state-of-the-art MLLMs, achieving over 95% task planning accuracy and delivering superior performance in tasks such as scene classification, object counting, and remote sensing visual question answering. Our work presents RS-Agent as a robust and extensible framework for advancing intelligent automation in remote sensing analysis.
Paper Structure (28 sections, 7 equations, 5 figures, 8 tables, 1 algorithm)

This paper contains 28 sections, 7 equations, 5 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Remote Sensing Multimodal Large Language Models
  4. AI Agent
  5. Retrieval-Augmented Generation
  6. Remote Sensing Agent (RS-Agent)
  7. Overall Architecture
  8. Central Controller
  9. Toolkit
  10. Solution Space
  11. Solution Database
  12. Solution Searcher with Task-Aware Retrieval
  13. Knowledge Space
  14. Knowledge Database
  15. Knowledge Searcher with DualRAG
  16. ...and 13 more sections

Figures (5)

  • Figure 1: The schematic diagram of the RS-Agent. RS-Agent consists of four main components: a Central Controller based on LLM, which comprehends user queries, maintains historical context, plans tool usage, aggregates results; a Solution Space that provides well-defined solutions to user-submitted problems; a Knowledge Space offering domain-specific knowledge in remote sensing; and a Toolkit integrating state-of-the-art methods for remote sensing tasks.
  • Figure 2: Framework of RS-Agent. The left side shows the overall architecture, including key components like the Central Controller, Solution Space, Knowledge Space, and Toolkit. The right side details the Solution Searcher $M_s$ and Knowledge Searcher $M_k$. The $M_s$ uses Task-Aware Retrieval method for solution guidance $g_s$ generation. The $M_k$ adopts a DualRAG strategy to retrieve relevant documents.
  • Figure 3: Qualitative result of RS-Agent. This figure shows several key tools that highlight the core capabilities of RS-Agent. The input image represents the image input by the user. The blue box shows the user's request, and the red box shows the RS-Agent's reply.
  • Figure 4: Illustration of the RS-Agent workflow with detailed prompt. The RS-Agent uses prompt1 to infer the task type of the query, while the Solution Searcher retrieves the Solution Guidance for that task. The RS-Agent then uses prompt2 to plan the workflow, execute the tools, and provide the final answer to the query.
  • Figure 5: The user interface of the RS-Agent. Users can upload remote sensing images and interpret remote sensing data through natural language. The example shows object detection and building damage detection task.