SOURCE-LINKED INTELLIGENCE

HSI-Road Relabeled: Surface-Aware Road-Scene Segmentation

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

The HSI-Road dataset provides paired RGB and 25-channel NIR (600--960~nm) 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 (SSMs) are evaluated under four input configurations: original-resolution RGB (RGB$_{\text{ori}}$), registered low-resolution RGB (RGB$_{\text{reg}}$), NIR, and channel-stacked RGB$_{\text{reg}}$--NIR (RGBN$_{\text{stk}}$). The comparison quantifies t

Read original source ↗ Open in workspace

recordType

paper

region

Global