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<p>We introduce Kust4K, a UAV-based dataset for RGB-TIR multimodal semantic segmentation. Kust4K dataset is designed to overcome key limitations in existing UAV-based semantic segmentation datasets: low information density, limited data volume, and insufficient robustness discussion under non-ideal environment. Kust4K dataset featuring 4,024 of 640 × 512 pixel-aligned RGB-Thermal Infrared image pairs captured across diverse urban road scenes under variable illumination. Extensive experiments with state-of-the-art models demonstrate Kust4K’s effectiveness, with multimodal training, significantly outperforming unimodal baselines. Additionally, these results highlight that multimodal image information is critical for obtaining more reliable semantic segmentation results. In total, Kust4K dataset advance robust urban traffic scene understanding, offering a valuable resource for intelligent transportation research.</p>

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An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

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  • Junlin Ouyang,
  • Qingwang Wang,
  • Ying Shang,
  • Pengcheng Jin,
  • Hangwei Zhong,
  • Liman Zhou,
  • Tao Shen

摘要

We introduce Kust4K, a UAV-based dataset for RGB-TIR multimodal semantic segmentation. Kust4K dataset is designed to overcome key limitations in existing UAV-based semantic segmentation datasets: low information density, limited data volume, and insufficient robustness discussion under non-ideal environment. Kust4K dataset featuring 4,024 of 640 × 512 pixel-aligned RGB-Thermal Infrared image pairs captured across diverse urban road scenes under variable illumination. Extensive experiments with state-of-the-art models demonstrate Kust4K’s effectiveness, with multimodal training, significantly outperforming unimodal baselines. Additionally, these results highlight that multimodal image information is critical for obtaining more reliable semantic segmentation results. In total, Kust4K dataset advance robust urban traffic scene understanding, offering a valuable resource for intelligent transportation research.