Urban Analytics

From 3D pedestrian networks to wheelable networks: An automatic wheelability assessment method for high-density urban areas using contrastive deep learning of smartphone point clouds
This paper presents a contrastive deep learning-based wheelability assessment method bridging street-scale smartphone point clouds and a city-scale 3D pedestrian network (3DPN). We reinforced the city-scale 3DPN using smartphone point clouds, a promising data source for supplementing fine-grain details and temporal changes due to the centimeter-level accuracy, vivid color, high density, and crowd sourcing nature.
Project DOI
From 3D pedestrian networks to wheelable networks: An automatic wheelability assessment method for high-density urban areas using contrastive deep learning of smartphone point clouds