Transportation asset management is a strategic approach to efficiently and cost-effectively manage transportation assets. Unfortunately, most current practices still rely on manual field surveys or windshield inspections that can be time-consuming, labor-intensive and subjective. Mobile Light Detection and Ranging (LiDAR), as an emerging remote sensing technology, has become cost-friendly and commercially available with better data quality. In this dissertation, an automated LiDAR data processing framework for network-level transportation asset management is proposed to address current challenges. It includes data acquisition and preprocessing, semantic segmentation, feature extraction, geometry measurement, GIS inventory and decision making. The proposed framework can support transportation agencies to manage asset efficiently and cost-effectively, fulfill local communities' needs and contribute to society's overall well-being.