Automated Roadside Vegetation Management Using LiDAR-Based Geospatial Simulations

Abstract Effective roadside vegetation management is vital for safety, visibility, and infrastructure longevity. Traditional methods are often labor-intensive, time-consuming, and difficult to scale. This paper presents a novel approach leveraging light detection and ranging (LiDAR) data and geospatial analysis for large-scale roadside vegetation management. We introduce the vegetation-free volume (VFV), a spatial boundary defining clear roadside space. Using high-resolution LiDAR point clouds and advanced geospatial processing, we detect and quantify overgrown vegetation encroaching into the VFV. The method transforms point clouds into a homogeneous coordinate system, applies geometric queries to identify breaches, and generates horizontal and vertical density profiles to visualize overgrowth. Structured machine- and human-readable reports provide actionable insights for automated systems and field crews. Furthermore, we simulate vegetation growth and pruning cycles at the LiDAR-point level to support maintenance scheduling and resource optimization. Results demonstrated that integrating LiDAR and geospatial analysis enhances precision, efficiency, and scalability in roadside vegetation management.