用街景图自动构建道路表面质量数据集，助力交通基础设施分析
SurfaceAI: Automated creation of cohesive road surface quality datasets based on open street-level imagery
- 基于公开街景数据训练模型，识别道路类型与质量
- 可生成覆盖整段道路的连贯表面状态信息
- 适合城市规划、智能交通系统研究者使用
本文提出SurfaceAI，一个从公开街景影像中自动生成道路表面类型与质量地理参考数据集的流程。道路不平整对交通参与者安全与舒适性影响显著，尤其对弱势道路使用者更为重要，因此需要详尽的道路表面数据支持基础设施建模与分析。SurfaceAI利用众包的Mapillary数据训练模型，预测街景图像中可见道路表面的类型与质量，并将结果聚合，提供整段道路状况的连贯信息。
原文摘要 · Abstract (English)
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.
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