Integrating BIM and UAV-based photogrammetry for Automated 3D Structure Model Segmentation

TL;DR

This paper presents a machine learning framework that combines UAV photogrammetry and BIM data to automate the segmentation of 3D infrastructure models, significantly improving efficiency and accuracy in structural health monitoring.

Contribution

It introduces a novel approach that integrates real UAV scans with synthetic BIM data for automated 3D model segmentation, reducing manual effort and training time.

Findings

High accuracy in segmenting railway components

Reduced training time with smaller datasets

Effective integration of UAV and BIM data

Abstract

The advancement of UAV technology has enabled efficient, non-contact structural health monitoring. Combined with photogrammetry, UAVs can capture high-resolution scans and reconstruct detailed 3D models of infrastructure. However, a key challenge remains in segmenting specific structural components from these models-a process traditionally reliant on time-consuming and error-prone manual labeling. To address this issue, we propose a machine learning-based framework for automated segmentation of 3D point clouds. Our approach uses the complementary strengths of real-world UAV-scanned point clouds and synthetic data generated from Building Information Modeling (BIM) to overcome the limitations associated with manual labeling. Validation on a railroad track dataset demonstrated high accuracy in identifying and segmenting major components such as rails and crossties. Moreover, by using smaller-scale datasets supplemented with BIM data, the framework significantly reduced training time while maintaining reasonable segmentation accuracy. This automated approach improves the precision and efficiency of 3D infrastructure model segmentation and advances the integration of UAV and BIM technologies in structural health monitoring and infrastructure management.