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New HSI-Road Dataset Enhances Road-Scene Segmentation with Surface Labels

By PulseAugur Editorial · [1 sources] ·

This paper introduces HSI-Road Relabeled, an enhanced version of the HSI-Road dataset for road-scene segmentation. The relabeled dataset includes a six-class taxonomy of surface types: Background, Asphalt, Concrete, Dirt, Water, and Grass. Researchers also developed an RGB-to-NIR registration pipeline and evaluated six semantic segmentation models under various input configurations, including original RGB, registered RGB, NIR, and a stacked RGB-NIR format. AI

IMPACT Provides a more detailed dataset for training and evaluating road-scene segmentation models, potentially improving autonomous driving systems.

RANK_REASON Publication of a new dataset and associated research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New HSI-Road Dataset Enhances Road-Scene Segmentation with Surface Labels

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Publication of a new dataset and associated research paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Imad Ali Shah, Imran Mehmood, Enda Ward, Martin Glavin, Edward Jones, Brian Deegan ·

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

    arXiv:2609.12151v1 Announce Type: new Abstract: 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,…

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