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This study extends the comparative analysis of geomatics methodologies by incorporating a European case study: a viaduct in L’Aquila, Italy. The objective is to assess the adaptability of LiDAR data acquisition by comparing two SLAM-based instruments for detecting and analyzing road surface conditions, including risks such as cracks. Surveys were conducted using the GeoSLAM ZEB HORIZON and the Leica BLK2GO, both equipped with a Leica G15 GNSS receiver. The collected datasets were processed following a structured workflow, which included ground and non-ground separation through Cloth Simulation and intensity attribute enhancement to evaluate potential improvements in crack detection accuracy. Crack detection was performed using the DBSCAN clustering method, with parameter optimization based on silhouette score evaluation and validation against manually labeled reference data. Additionally, a Random Forest classifier was applied as a benchmark for comparative analysis. The research investigates the capability of SLAM-based mobile mapping systems to support reliable and automated crack detection on road surfaces, thereby contributing to the improvement of current infrastructure monitoring practices. The findings align with European standards, such as those established by the Italian Ministry of Infrastructure and Transport (MIT), which emphasize systematic risk classification for maintenance prioritization. While traditional assessments in Italy rely on manual inspections, automated approaches are gaining traction, mirroring broader European efforts to integrate advanced technologies for road safety. Overall, this study demonstrates the potential of lightweight solutions for cost-effective, accurate, and repeatable road infrastructure assessments.

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Evaluating Viaduct Surface Cracking on SLAM Point Clouds: Insights from an Italian Case Study

  • 会议论文

  • 发表日期：

    2026年

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  • N. Pascucci,
  • M. Alicandro,
  • S. Zollini,
  • D. Dominici

摘要

This study extends the comparative analysis of geomatics methodologies by incorporating a European case study: a viaduct in L’Aquila, Italy. The objective is to assess the adaptability of LiDAR data acquisition by comparing two SLAM-based instruments for detecting and analyzing road surface conditions, including risks such as cracks. Surveys were conducted using the GeoSLAM ZEB HORIZON and the Leica BLK2GO, both equipped with a Leica G15 GNSS receiver. The collected datasets were processed following a structured workflow, which included ground and non-ground separation through Cloth Simulation and intensity attribute enhancement to evaluate potential improvements in crack detection accuracy. Crack detection was performed using the DBSCAN clustering method, with parameter optimization based on silhouette score evaluation and validation against manually labeled reference data. Additionally, a Random Forest classifier was applied as a benchmark for comparative analysis. The research investigates the capability of SLAM-based mobile mapping systems to support reliable and automated crack detection on road surfaces, thereby contributing to the improvement of current infrastructure monitoring practices. The findings align with European standards, such as those established by the Italian Ministry of Infrastructure and Transport (MIT), which emphasize systematic risk classification for maintenance prioritization. While traditional assessments in Italy rely on manual inspections, automated approaches are gaining traction, mirroring broader European efforts to integrate advanced technologies for road safety. Overall, this study demonstrates the potential of lightweight solutions for cost-effective, accurate, and repeatable road infrastructure assessments.