Monitoring biological crusts in civil engineering structures using intensity data from terrestrial laser scanners

Terrestrial laser scanning is becoming a standard for 3D recording and modeling of complex and remote engineering structures. Results of the scan contain detailed geometric information about the scene. However, the lack of intensity details is still a limitation in making these data useable for monitoring and analysing engineering structures. This paper proposes a methodology for extracting information about the presence of biological crusts on concrete structures using terrestrial laser scanners. The goal of this methodology is to integrate all the available information, range, intensity and color, into the extraction workflow. The methodology is based primarily on two algorithms. The first algorithm allows building an orthoimage using the intensity data obtained from the scanners: a Riegl LMS Z390i and a Trimble GX. A supervised study, comparing some areas with evidence of biological crusts with the data shown in a RGB ortho-image, was performed by a human expert and was considered to be ground. The Riegl system yielded better results than the Trimble system, with errors of approximately 20% in the worst case, which is acceptable for such studies. The second algorithm tests two different classifiers, called K-means and Fuzzy C-means, to automatically extract information about the areas of concrete where biological crusts present. In both cases, data obtained from the Riegl scanner were closer to the real situation than data obtained from the Trimble scanner. These differences are attributable to the different wavelengths of the lasers.

► Detection of biological crusts in civil engineering using the intensity data from laser scanners.
► Automation of the methodology using image classification techniques.
► Comparison between different scanners that work with different wavelengths.