Lidar change detection identifies differences in the landscape such as subsidence, erosion, and vegetation encroachment, which are crucial for proactive pipeline management. With rapid advancements in lidar technology, the volume of data required for change detection demands extensive computational resources, necessitating significant manual effort that can delay decision-making.

This study presents a framework for automated 3D point cloud analysis, emphasizing lidar change detection for pipeline monitoring. As infrastructure increasingly requires efficient management, our approach leverages high-resolution lidar and photogrammetry data to facilitate rapid change detection, enhancing operational efficiency from data acquisition to decision-making.

The change detection algorithm is derived from Multiscale Model to Model Cloud Comparison with Iterative Closest Point (M3C2-ICP) techniques. These techniques involve compute-intensive processing that benefits from the use graphics processing units (GPUs) to determine alignment and accurate comparison of sequential point clouds, revealing subtle changes over time. By automating the algorithm, we minimize manual intervention and significantly reduce analysis time, achieving results in minutes rather than days.

Furthermore, the output data is automatically ingested into Cambio, a cloud-based platform that translates the Digital Elevation Model (DEM) and change detection outputs into multiple intuitive terrain visualizations and dynamic limited of detection (LoD) legends. This platform empowers stakeholders to quickly interpret changes and make informed decisions regarding maintenance and risk mitigation.

Our findings, validated across more than 150,000 km²

of change detection, demonstrate the effectiveness of the M3C2-ICP approach by streamlining advanced visualization to Cambio, providing a powerful tool for pipeline monitoring. By integrating change detection and visualization, this framework not only improves the efficiency of infrastructure management, but also sets a new standard for near real-time monitoring practices. The results highlight the transformative potential of automated 3D point cloud analysis in enhancing operational efficiency and decision-making in the pipeline industry.