Urban pavement condition assessment with hyperspectral remote sensing: a case study of concrete pavements

Purpose Concrete pavements are vital to transportation infrastructure, yet their timely condition assessment remains a major challenge because conventional monitoring techniques, such as visual inspection and the Pavement Condition Index (PCI), are often costly, time-consuming, and spatially constrained. This study examines the applicability and reliability of hyperspectral remote sensing as a non-destructive and scalable method for assessing the condition of concrete pavements. Design/methodology/approach A total of 36 field spectrometer data and Airborne Visible and Infrared Imaging Spectrometer–Next Generation imagery were integrated to analyze concrete pavements. Reflectance data from normal and cracked surfaces were compared to identify diagnostic absorption features and quantify spectral indices, including normalized indices, near-infrared (NIR), and shortwave infrared (SWIR) ratios. Findings The analysis revealed distinct absorption bands at approximately 478 nm, 1,060 nm, and 2,207 nm, corresponding to iron oxide, hygroscopic water, and clay minerals, respectively. Compared with normal surfaces, cracked pavements exhibited a 10–15% decrease in overall reflectance and a significant increase in spectral indices. Regression analysis demonstrated a strong field-to-image correlation (R2 = 0.77–0.81), confirming methodological reliability. Research limitations/implications The study is limited to one urban context; validation is required across diverse regions, materials, and traffic conditions. Practical implications The approach provides a robust and cost-effective method for assessing pavement conditions. Combining field spectroscopy with airborne hyperspectral imagery enables the early detection of deterioration and scalable monitoring to support innovative infrastructure management. Originality/value This research presents a novel framework for monitoring concrete pavements in India, demonstrating the potential of hyperspectral remote sensing to enhance large-scale urban road assessments.