Abstract
This paper introduces SurfaceAI, a pipeline designed to generate comprehensive georeferenced datasets on road surface type and quality from openly available street-level imagery. The motivation stems from the significant impact of road unevenness on the safety and comfort of traffic participants, especially vulnerable road users, emphasizing the need for detailed road surface data in infrastructure modeling and analysis. SurfaceAI addresses this gap by leveraging crowdsourced Mapillary data to train models that predict the type and quality of road surfaces visible in street-level images, which are then aggregated to provide cohesive information on entire road segment conditions.
Abstract (translated)
本文介绍了SurfaceAI，一种旨在从公开可用的道路层面图像中生成全面的几何参考数据集的管道。动机源于道路不平整对交通参与者安全和舒适性的重大影响，尤其是对脆弱的道路用户。强调在基础设施建模和分析中需要详细的道路表面数据。SurfaceAI通过利用开源的Mapillary数据集训练预测模型，模型可以预测道路层面图像中可见的 road surface 类型和质量，然后汇总以提供关于整个道路段状况的统一信息。
URL
https://arxiv.org/abs/2409.18922
