Fotogrametría aérea para la identificación de fallas en pavimentos flexibles

Abstract

DOI: https://doi.org/10.46296/ig.v9i17.0337 Abstract Pavement condition assessment is a fundamental task for road infrastructure management. Traditionally, this process is carried out through field visual inspections, which involve high costs, significant time requirements, and safety risks for technical personnel. In this context, this study aims to identify and classify failures in flexible pavements using aerial photogrammetry techniques based on unmanned aerial vehicles (UAVs). The methodology included aerial image acquisition, orthomosaic generation, digital image processing, and supervised classification to detect different types of pavement distress. The results were compared with the traditional visual inspection method based on the Pavement Condition Index (PCI). The results allowed the identification of several types of pavement distress, including cracks, patches, potholes, and weathering, reaching a kappa index of 0.81. However, the comparison between visual inspection and photogrammetric classification showed an overall accuracy of 58%. Cracks and patches achieved the highest accuracy levels, while other failures were more difficult to distinguish due to spectral similarities and environmental factors. The study concludes that UAV-based photogrammetry represents a promising alternative for pavement monitoring, especially for preliminary assessments and large-scale surveys. Nevertheless. Keywords: UAV photogrammetry, flexible pavements, pavement distress, remote sensing, image classification.DOI: https://doi.org/10.46296/ig.v9i17.0337 Resumen La evaluación de la condición de los pavimentos es una actividad fundamental para la gestión de infraestructuras viales. Tradicionalmente, este proceso se realiza mediante inspecciones visuales en campo, lo que implica altos costos, tiempo considerable y riesgos para el personal técnico. En este contexto, el presente estudio tiene como objetivo identificar y clasificar fallas en pavimentos flexibles mediante técnicas de fotogrametría aérea utilizando vehículos aéreos no tripulados (VANT). La metodología empleada incluyó la captura de imágenes aéreas, generación de ortomosaicos, procesamiento digital de imágenes y clasificación supervisada para identificar diferentes tipos de deterioro superficial. Posteriormente, los resultados fueron comparados con el método tradicional de inspección visual basado en el índice PCI (Pavement Condition Index). Los resultados permitieron identificar fallas como fisuras, parches, baches y meteorización, alcanzando un índice de concordancia kappa de 0,81. Sin embargo, la comparación entre la inspección visual y la clasificación fotogramétrica mostró una coincidencia global del 58 %. Las fisuras y parches presentaron mayores niveles de precisión, mientras que otras fallas se confundieron debido a similitudes espectrales y condiciones de iluminación. Se concluye que la fotogrametría aérea constituye una alternativa viable para el monitoreo de pavimentos, especialmente en procesos de evaluación preliminar y levantamientos de grandes extensiones de vías. No obstante, el método tradicional basado en inspección visual sigue siendo el más completo para la identificación detallada de fallas. Palabras clave: fotogrametría aérea, pavimentos flexibles, VANT, teledetección, clasificación de imágenes, PCI. Abstract Pavement condition assessment is a fundamental task for road infrastructure management. Traditionally, this process is carried out through field visual inspections, which involve high costs, significant time requirements, and safety risks for technical personnel. In this context, this study aims to identify and classify failures in flexible pavements using aerial photogrammetry techniques based on unmanned aerial vehicles (UAVs). The methodology included aerial image acquisition, orthomosaic generation, digital image processing, and supervised classification to detect different types of pavement distress. The results were compared with the traditional visual inspection method based on the Pavement Condition Index (PCI). The results allowed the identification of several types of pavement distress, including cracks, patches, potholes, and weathering, reaching a kappa index of 0.81. However, the comparison between visual inspection and photogrammetric classification showed an overall accuracy of 58%. Cracks and patches achieved the highest accuracy levels, while other failures were more difficult to distinguish due to spectral similarities and environmental factors. The study concludes that UAV-based photogrammetry represents a promising alternative for pavement monitoring, especially for preliminary assessments and large-scale surveys. Nevertheless. Keywords: UAV photogrammetry, flexible pavements, pavement distress, remote sensing, image classification. Información del manuscrito:Fecha de recepción: 13 de octubre de 2025.Fecha de aceptación: 18 de diciembre de 2025.Fecha de publicación: 12 de enero de 2026