mlsanityData to 7 Oct 2026

SurfaceAI: Automated creation of cohesive road surface quality datasets based on open street-level imagery

arXiv 2409.18922 ↗First posted 27 Sep 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 27 Sep 2024.

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References · 3

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  3. 2019Seamless Scene Segmentationcited by 112

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