mlsanityData to 7 Oct 2026

A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation

arXiv 2606.28896 ↗First posted 27 Jun 2026eess.IV cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → Multimodal and embodied agents

Abstract (arXiv)

Synthetic aperture radar (SAR) data augmentation is important for improving the generalization of data-driven SAR interpretation models, yet practical augmentation workflows are often hindered by heterogeneous dataset formats, task-dependent metadata requirements, diverse generation methods, and weak validation of generated samples. This paper presents the SAR Augmentation and Generation Agent (SAGA), a schema-grounded and benefit-aware agent framework for task-oriented SAR data generation and augmentation. Given a natural-language request and heterogeneous SAR inputs, SAGA extracts observable dataset facts, validates executable dataset schemas, selects feasible augmentation strategies through validator-constrained planning, and compiles the selected strategy into an auditable augmentation workflow. Generated data are further assessed by quality, distribution, SAR-artifact, duplicate, leakage, and optional downstream-task evaluators to support evidence-qualified augmentation claims. By separating semantic proposal from deterministic validation and execution, SAGA improves the reliability and reproducibility of SAR augmentation decisions. Experiments on controlled agentic benchmarks and downstream SAR interpretation tasks show that SAGA improves schema grounding, skill planning, invalid-sample rejection, and downstream augmentation utility compared with rule-based, LLM-only, ReAct-style, and fixed-augmentation baselines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 29 of its 40 references that are in the map. First posted on arXiv 27 Jun 2026.

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29 of 40 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks29 May
  2. 2024An LLM Agent for Automatic Geospatial Data Analysis24 Oct
  3. 2024RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent11 Jun

Other references · 26

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14 more
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