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RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

Wenjia Xu, Zijian Yu, Boyang Mu, Jiuniu Wang, Zhiwei Wei, Mugen Peng

arXiv:2406.07089v4cs.CV

TL;DR

Remote-sensing MLLMs have difficulty with expert interpretation and analytical reasoning, while existing agents can lack unified procedural and domain-knowledge support. RS-Agent combines LLM planning, specialized tools, expert-defined solutions, and domain-knowledge retrieval; across 9 datasets and 18 tasks, it reports over 95% task planning accuracy and performance above prior MLLMs.

  • Problem

    Remote-sensing MLLMs struggle with expert interpretation and analytical reasoning, and prior agents can lack unified support for diverse tools, procedural reasoning, and specialized knowledge.

    Source: §1 · pp. 1–2

  • Method

    RS-Agent combines an LLM-based controller and toolkit with expert-defined task procedures and DualRAG domain-knowledge retrieval.

    Source: §1 · pp. 2–3

  • Results

    Across 9 datasets and 18 remote sensing tasks, RS-Agent achieves over 95% task planning accuracy and consistently surpasses state-of-the-art MLLMs across multiple benchmarks.

    Source: §1 · p. 2, §5 · p. 15

  • Takeaways & Limitations

    RS-Agent shows potential to streamline remote-sensing workflows, reduce reliance on domain experts, and support accessible, high-quality real-world analysis.

    Source: §5 · p. 15

    Task inference can over-abstract queries and select incorrect tools, tool arguments can be malformed, and current metrics do not assess intermediate workflow quality at fine granularity.

    Source: §5 · p. 15

Abstract

from arXiv · show

Recent advances in Multimodal Large Language Models (MLLMs) have shown promise for remote sensing tasks such as visual question answering and scene understanding. However, existing models remain limited to basic instruction-following and struggle with real-world scenarios that require multi-source data integration, fine-grained spatial reasoning, and domain expertise. To address this gap, we propose RS-Agent, a domain-adapted intelligent agent that connects user intent with professional remote sensing workflows through structured task planning and tool orchestration. RS-Agent consists of four components aligned with typical remote sensing workflows: a Central Controller for intent understanding and process planning, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain knowledge support. We further introduce Task-Aware Retrieval, which improves planning by identifying task types and retrieving expert-defined solutions, and DualRAG, a weighted dual-path retrieval-augmented generation method that enhances the relevance and completeness of retrieved knowledge. RS-Agent natively supports multiple imaging modalities, including optical and SAR imagery, and can automatically organize dedicated SAR processing tools into executable workflows. Experiments on 9 datasets and 18 remote sensing tasks show that RS-Agent significantly outperforms state-of-the-art MLLMs, achieving over 95% task planning accuracy and strong results in scene classification, object counting, and remote sensing visual question answering. These results demonstrate the value of combining LLM reasoning with remote sensing expertise for intelligent geospatial analysis.

1 Introduction

RS-Agent combines LLM reasoning with remote-sensing expertise to orchestrate tools for professional workflows. Its task-aware planning and domain-knowledge retrieval address gaps in existing models, and experiments report strong performance across tasks.

1 Introduction

  • The paper identifies MLLMs’ difficulty with expert interpretation of diverse remote-sensing imagery and analytical reasoning as a motivation for domain-aware agents.It also describes prior remote-sensing agents as limited in scaling to diverse tools and structured procedural reasoning.Source: §1 · pp. 1–2
  • RS-Agent integrates a query-planning Central Controller, a toolkit, a task-specific Solution Space, and a domain-knowledge Knowledge Space.The components mirror typical remote sensing workflows.Source: §1 · p. 2, Figure 1 · p. 2
  • Task-Aware Retrieval infers task types and retrieves expert-defined procedures to guide tool selection and step-by-step task decomposition.The Solution Database stores professional knowledge for tool use.Source: §1 · pp. 2–3
  • Across 9 datasets and 18 remote sensing tasks, RS-Agent achieves over 95% task planning accuracy and surpasses RS-ChatGPT and single-model performance on several tasks.The reported tasks include scene classification, remote sensing visual question answering, and object counting.Source: §1 · p. 2
  • DualRAG combines weighted keyword-aware retrieval with global semantic search to retrieve relevant domain knowledge for geospatial tasks.Its dual paths address queries that need both macro-level context and fine-grained technical detail.Source: §1 · pp. 2–3

2 Related Works

Prior remote-sensing MLLMs and agents have expanded task coverage, but limitations in reasoning, procedural guidance, and retrieval remain. This section reviews those approaches and motivates RS-Agent’s expert-guided architecture and DualRAG.

2.1 Remote Sensing Multimodal Large Language Models

  • End-to-end MLLMs can struggle with deep reasoning, quantitative tasks such as object counting, and updating static knowledge without retraining.Remote-sensing adaptations support dialogue, localization, multi-task reasoning, and temporal understanding.Source: §2.1 · p. 3

2.2 LLM-based Agents in Remote Sensing

  • Most remote-sensing agent baselines use generic paradigms and static parametric memory, while RS-Agent adds expert procedural guidance and structured knowledge retrieval.Earlier agents include narrow specialists and broader tool-chaining systems.Source: §2.2 · p. 3

2.3 Retrieval-Augmented Generation

  • Uniform keyword concatenation can dilute critical technical constraints in remote-sensing queries, motivating DualRAG’s weighted keyword retrieval alongside holistic query retrieval.The section also notes that standard dense or sparse retrieval can falter on multi-hop or global-aggregation tasks.Source: §2.3 · p. 3

3 Remote Sensing Agent (RS-Agent)

RS-Agent turns remote-sensing queries and images into answers or processed outputs through task-centric, multi-stage reasoning. Its architecture retrieves task-specific workflows and geospatial context before orchestrating tools.

3.1.1 Problem Formulation

  • RS-Agent models remote-sensing analysis as multi-stage reasoning over task type, expert solution guidance, and domain knowledge to bridge user intent and tool execution.It maps a query and image to a textual answer and, optionally, a processed visual output.Source: §3.1.1 · p. 4

3.1.2 Architectural Design

  • Four components divide the work among a controller, extensible toolkit, expert-guidance Solution Space, and domain-knowledge Knowledge Space.The controller plans, retrieves support, orchestrates tools, and synthesizes responses; the toolkit spans optical and SAR modalities and multiple analysis levels.Source: §3.1.2 · pp. 4–5

3.2 Central Controller

  • The Central Controller first infers supported task types from query attributes, then uses retrieved guidance and dialogue history to plan and execute a tailored tool workflow.Its task inference considers modality, spatial granularity, temporal configuration, and analytical operation.Source: §3.2 · p. 5

3.3 Toolkit

  • The Toolkit organizes tools by sensing modality, analysis level, and geometric or semantic characteristics, exposing them through standardized APIs for modular orchestration.The current implementation includes 27 tools and is extensible to additional tools.Source: §3.3 · pp. 5–6

3.4 Solution Space / 3.4.1 Solution Database

  • The Solution Space addresses ambiguous or underspecified instructions with expert-curated procedures, and its database combines expert-designed solutions, generated variants, and execution checks.Its 34 documents comprise 27 single-tool templates and 7 multi-tool workflow templates, including composite RSVQA pipelines.Source: §3.4 · p. 6, §3.4.1 · p. 6

3.4.2 Solution Searcher with Task-Aware Retrieval

  • Task-Aware Retrieval infers a task type from the query and uses that type, rather than the raw query, to retrieve matching expert-defined tool solutions.Retrieved guidance supports tool selection and reasoning for complex remote-sensing tasks.Source: §3.4.2 · pp. 6–7

3.5 Knowledge Space / 3.5.1 Knowledge Database

  • The Knowledge Space retrieves curated remote-sensing knowledge, stored as entity–relation triples in vector spaces to support structure-aware retrieval and multi-hop reasoning.The general corpus covers sensing modalities, platforms, targets, observation principles, and domain semantics; RSaircraft is a case-study subset covering 65 aircraft categories.Source: §3.5.1 · p. 7, §3.5 · p. 7

3.5.2 Knowledge Searcher with DualRAG

  • DualRAG weights domain-relevant keywords and combines global semantic search with per-keyword retrieval to capture both query-wide context and fine-grained details.Keyword-level document quotas follow keyword weights, while the global and keyword results are deduplicated when combined as knowledge guidance.Source: §3.5.2 · p. 8

4 Experiments

The experiments evaluate RS-Agent’s task planning and remote-sensing applications, then test Task-Aware Retrieval, DualRAG, and qualitative multi-step reasoning. Results cover comparisons with RS-ChatGPT and single-model baselines, multiple LLM configurations, and two retrieval ablations.

4.2.1 Task Planning Accuracy Compared to SOTA Agent

  • 84.89% accuracy with RS-Agent versus 72.66% with RS-ChatGPT using gpt-3.5-turbo-1106 (+12.23%).RS-Agent also gains +5.76% with gpt-3.5-turbo and +7.19% with gpt-4o-mini.Source: Table 1 · p. 9, §4.2.1 · pp. 9–10

4.2.2 Task Planning Accuracy with Different LLMs

  • Across 10 representative optical, SAR, and cross-modal tasks, Qwen2.5-72B and 32B reach 97.50% and 96.50% planning accuracy, respectively, matching or exceeding the ChatGPT series.The full evaluation dataset contains 360 instances across 18 tasks; the paper notes performance varies, especially when tool boundaries are unclear.Source: Table 2 · p. 9, §4.2.2 · p. 10

4.3.1 Object Counting

  • RS-Agent leads GeoChat and LHRS-Bot on all three object-counting metrics: 33.30% absolute accuracy, 75.98% interval-matching accuracy, and 0.28 relative error.The reported approach decomposes counting into detection and aggregation using a specialized detector.Source: Table 3 · p. 10, §4.3.1 · p. 11

4.3.3 Scene Classification

  • RS-Agent outperforms all baselines on scene classification: 98.00% on RSSDIVCS, 96.88% on AID, and 98.63% on UCMerced.The agent routes images to a fine-tuned domain classifier; evaluation spans three datasets.Source: Table 5 · p. 10, §4.3.3 · p. 11

4.3.2 Visual Question Answering

  • RS-Agent’s multi-tool zero-shot VQA scores are 97.00% on Rural/Urban, 91.07% on Presence, and 90.58% on Comparison, averaging 90.88%, the highest among tested models.The evaluation uses RSVQA-LR and compares general-purpose and remote-sensing-specific MLLMs.Source: Table 4 · p. 10, §4.3.2 · p. 11

4.4 Effectiveness of the Task-Aware Retrieval method

  • 93.53% single-tool and 90.62% multi-tool accuracy result when Task Inference and Solution Retrieval are combined, versus 69.06% and 9.42% for the baseline.The toolset is identical across variants; the reported gains are attributed to orchestration. Table 7 cases illustrate that incorrect tool selection or execution order can lead to failure.Source: Table 6 · p. 11, §4.4 · p. 13

4.5 Effectiveness of the DualRAG method

  • DualRAG consistently surpasses LightRAG, with larger gains on RSaircraft; Hybrid mode shows gaps of +26.40% in Comprehensiveness, +25.00% in Empowerment, and +28.00% in Overall.The comparison uses RSaircraft and Mix datasets and evaluates four response dimensions under Local, Global, and Hybrid keyword modes.Source: Table 8 · p. 14, §4.5 · p. 14

4.6 Qualitative Results

  • Qualitative examples show that RS-Agent interprets underspecified requests, such as “What do you see in this image?”, as image-description tasks and invokes an appropriate captioning model.The paper describes the modular toolkit as supporting integration of new tools.Source: Figure 4 · p. 12, §4.6 · p. 14

5 Conclusion

RS-Agent combines planning, tool execution, context-aware memory, and retrieval to support remote-sensing workflows. Experiments show broad benchmark gains, while the authors identify workflow and knowledge-coverage limitations.

5 Conclusion

  • RS-Agent integrates user-query understanding, complex task planning, tool execution, and context-aware memory for remote-sensing applications.Its architecture is designed to support diverse tasks and imaging modalities.Source: §5 · pp. 14–15
  • Task-Aware Retrieval improves task-planning accuracy, while DualRAG provides specialized domain knowledge for complex technical queries.The conclusion also reports that RS-Agent can integrate new tools while retaining previously learned capabilities.Source: §5 · pp. 14–15
  • RS-Agent consistently outperforms state-of-the-art multimodal large language models across multiple remote-sensing benchmarks.The authors say the system has potential to streamline workflows, reduce reliance on domain experts, and enable more accessible analysis.Source: §5 · p. 15
  • Task inference can over-abstract queries, tool arguments can be malformed, and upstream errors may propagate through multi-tool workflows.The authors also note limited, manually curated Knowledge and Solution Databases and evaluation metrics that do not diagnose intermediate-step quality finely.Source: §5 · p. 15

A Detailed Prompt Designs

The appendix details prompts that guide task inference and weighted keyword extraction using remote-sensing domain cues. They constrain outputs to supported task types and structured formats.

A Detailed Prompt Designs

  • The prompt templates incorporate remote-sensing domain knowledge to guide the Central Controller’s task-planning decisions.The appendix covers task inference and keyword extraction prompts.Source: A Detailed Prompt Designs · p. 18

A.1 Task Inference Prompt

  • The task-inference prompt selects the minimal sufficient set of supported task types from the user’s remote-sensing intent.It asks the model to analyze the query as a remote-sensing expert.Source: §A.1 · p. 18
  • Task inference considers sensor or modality, spatial granularity, temporal setting, and analytical operation.The listed operations include detection, segmentation, classification, change analysis, counting, and description or question answering.Source: §A.1 · p. 18
  • When query evidence is insufficient, the prompt permits knowledge search before task re-evaluation and favors high-level tasks over redundant steps.This optional step is conditioned on the task type not being confidently inferable from the query alone.Source: §A.1 · p. 18
  • A consistency check requires task types to come from the supported list and form a minimal, non-redundant, sufficient set.Source: §A.1 · p. 18
  • The task-inference prompt takes the supported tasks and user query as inputs and returns task types in JSON.Source: §A.1 · p. 18

A.2 Keyword Extraction Prompt

  • The DualRAG keyword prompt extracts concise searchable terms and assigns importance scores, prioritizing remote-sensing terminology over trivial words.The prompt asks for scores from 1 to 10.Source: §A.2 · p. 18
  • Keyword prioritization ranks task or analysis target highest and location lowest; scores distinguish core constraints, qualifiers, and broad context.The output format is a JSON list of terms paired with importance scores.Source: §A.2 · p. 18

B Multi-Tool End-to-End Task Success Rate

On RSVQA-LR, the full Task Inference plus Solution Retrieval configuration achieved higher end-to-end task success than variants using either component alone or neither.

B Multi-Tool End-to-End Task Success Rate

C Complete Task Planning Accuracy

The supplement provides complete task-planning accuracy results for 18 evaluation tasks and reports that LLM rankings remain consistent with the main-text subset.

C Complete Task Planning Accuracy

D Tool List

The RS-Agent toolkit comprises 27 tools organized by sensing modality and analysis level.

D Tool List

E Multi-Tool Collaboration Example

RS-Agent handles complex queries through a coordinated workflow that infers task type, retrieves task-specific guidance, and plans tool execution.

E Multi-Tool Collaboration Example

F User Interface

The RS-Agent interface lets users upload remote sensing images and request analyses in natural language, including object detection and building damage assessment.

F User Interface

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