<b><i>Kust4K:</i></b><i> </i><b><i>An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation</i></b>

We introduce Kust4K, a multimodal semantic segmentation dataset, which is the first and largest UAV-based dataset for RGB-TIR 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.我们推出了Kust4K——一款多模态语义分割（multimodal semantic segmentation）数据集，它也是首个且规模最大的基于无人机（Unmanned Aerial Vehicle, UAV）的红-热红外（RGB-TIR）语义分割数据集。Kust4K数据集旨在解决现有基于无人机的语义分割数据集存在的核心局限：信息密度不足、数据体量有限，且未充分探讨非理想环境下的模型鲁棒性问题。该数据集包含4024对分辨率为640×512的像素对齐型红-热红外图像对，采集场景覆盖不同光照条件下的多样城市道路场景。针对该数据集开展的大量当前最优（state-of-the-art）模型实验证实了Kust4K的有效性：采用多模态训练的模型性能显著优于单模态基准模型。此外，实验结果亦凸显了多模态图像信息对于获取更可靠的语义分割结果至关重要。总体而言，Kust4K数据集可助力实现更具鲁棒性的城市交通场景理解，为智能交通研究提供了宝贵的学术资源。