ArticleScientific data2025
An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- An RGB-TIR Dataset from UAV Platform for Robust Urban Traffic Scenes Semantic Segmentation.Scientific data · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
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