HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation
The HSI-Road dataset provides paired RGB and 25-channel NIR (680-960nm) images with binary masks but no surface-level labels. This paper introduces a manually labeled six-class taxonomy: Background, Asphalt, Concrete, Dirt, Water, and Grass, and an RGB-to-NIR registration pipeline with corresponding annotations. Six semantic-segmentation models are evaluated under four input configurations: original-resolution RGB, registered low-resolution RGB (RGB$_{\text{reg}}$), NIR, and channel-stacked RGB$_{\text{reg}}$-NIR (RGBN$_{\text{stk}}$). The comparison quantifies the effect of spatial-resolution reduction on RGB, along with evaluation of NIR and RGBN$_{\text{stk}}$, with results reported us...
| Authors | Imad Ali Shah, Imran Mehmood, Enda Ward et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 September 2026 |
| Last updated at source | 12 October 2026 |
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