Abstract. This study addresses the challenges of accessibility and laborious intensity in visual inspections of public metropolitan mobility infrastructure, such as elevated Metro systems. It explores an experimental 3D-Mapping Inspection and Classification Evaluation method (3D-MICE) utilizing UAV imagery and geometric mensuration from 3D point clouds. The method introduces two classification techniques: Condition Classification by Intensity (CCI) and Geometry Classification by RGB color (GCC), applied to orthomosaics. 3D-MICE enables semi-automatic detection, segmentation, and measurement of cracks and stains in reinforced concrete by selecting areas of interest based on intensity and geometric features. This approach offers a promising, efficient, and precise alternative to traditional inspection methods. 3D-MICE can detect, segment and measure, semi-automatically, cracks and stains of reinforced concrete structures by selecting areas of interest based on intensity and geometry.
Yépez-Rincón et al. (2026) studied this question.