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Related Experiment Video

Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Multi-Weather DomainShifter: A Comprehensive Multi-Weather Transfer LLM Agent for Handling Domain Shift in Aerial

Yubo Wang1, Ruijia Wen1, Hiroyuki Ishii1

  • 1Department of Modern Mechanical Engineering, Waseda University, Tokyo 169-8555, Japan.

Journal of Imaging
|November 26, 2025
PubMed
Summary

Domain shifts from changing weather degrade remote sensing models. Multi-Weather DomainShifter uses synthetic data and AI to adapt models to new conditions without extra annotation, improving performance in varied aerial imagery.

Keywords:
aerial image processingdomain shiftimage generationlarge language model agentsemantic segmentationstyle transfersynthetic data

Related Experiment Videos

Last Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Deep learning models in remote sensing face performance decline due to domain shifts from varying illumination, atmospheric conditions, and scene changes.
  • Adapting aerial image segmentation models is difficult due to the high cost and scarcity of annotated training data for diverse weather conditions.

Purpose of the Study:

  • To develop a comprehensive multi-weather domain transfer system, Multi-Weather DomainShifter, for augmenting single-domain aerial images into various weather conditions.
  • To enable domain adaptation without requiring additional laborious data annotation, coordinated by a large language model (LLM) agent.

Main Methods:

  • Generation of a synthetic dataset using Unreal Engine with diverse weather conditions (overcast, foggy, dusty).
  • Implementation of a latent space style transfer model for creating alternate domain versions from real aerial datasets.
  • Development of a multi-modal snowy scene diffusion model with LLM-assisted scene descriptors for incorporating snowy elements.
  • Integration of these methods into a tool library managed by an LLM agent for automated tool selection and execution.

Main Results:

  • Domain shift due to weather variations significantly degrades aerial image segmentation model performance.
  • The proposed Multi-Weather DomainShifter effectively adapts models to perform well in shifted domains (e.g., overcast, foggy, dusty, snowy).
  • The system maintains model effectiveness in the original domain while improving performance in augmented weather conditions.

Conclusions:

  • Multi-Weather DomainShifter offers a robust solution for addressing domain shift challenges in remote sensing image analysis.
  • The LLM-coordinated system successfully generates diverse weather conditions, enhancing model adaptability and performance without extensive manual annotation.
  • This approach significantly improves the resilience and applicability of deep learning models in real-world, variable remote sensing scenarios.