# M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data

Source: [M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data](<https://www.sota2.com/research/paper/68fa780e1e209131cea76914>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:31:26.317Z

Authors: Tzu-Yun Tseng, Hongyu Lyu, Josephine Li, Julie Stephany Berrio, Mao Shan, Stewart Worrall

Published: 2025-04-14T11:32:01.000Z

## Abstract

Road damage can create safety and comfort challenges for both human drivers and autonomous vehicles (AVs). This damage is particularly prevalent in rural areas due to less frequent surveying and maintenance of roads. Automated detection of pavement deterioration can be used as an input to AVs and driver assistance systems to improve road safety. Current research in this field has predominantly focused on urban environments driven largely by public datasets, while rural areas have received significantly less attention. This paper introduces M2S-RoAD, a dataset for the semantic segmentation of different classes of road damage. M2S-RoAD was collected in various towns across New South Wales, Australia, and labelled for semantic segmentation to identify nine distinct types of road damage. This dataset will be released upon the acceptance of the paper.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.10123>)

[PDF](<https://arxiv.org/pdf/2504.10123.pdf>)