Semantic enrichment of urban space: A point cloud-based framework for road marking analysis

Abstract

In this paper, we present a method for the analysis of urban road markings using point cloud data, aiming to enhance the understanding and functionality of urban spaces. Our method employs intensity-based analysis along with hierarchical clustering and object descriptors to identify and categorize road markings while maintaining the original point cloud format. Then, we conceptualize a new space distribution from segmenting urban spaces into distinct functional areas based on road markings. Our results indicate the potential of our approach in providing insights that could support urban planning, and autonomous vehicle navigation systems, yet further research and validation are essential to fully realize its potential.