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
ori), registered low-resolution RGB (RGB
reg), NIR, and channel-stacked RGB
reg--NIR (RGBN
stk). The comparison quantifies the effect of spatial-resolution reduction on RGB, along with evaluation of NIR and RGBN
stk, with results reported using per-class and mean IoU and F1 scores. RGB
ori achieves the highest overall performance but contains 12
× more pixels than the matched-resolution inputs. At the matched 192
×384 resolution, RGBN
stk outperforms NIR for all six SSMs and RGB
reg for five of six, with the most consistent gains for the Water class. These results highlight the importance of spatial resolution while showing that NIR provides complementary information to RGB.