An Integrated Remote Sensing and GIS Road Condition Assessment Framework

Road infrastructure is essential for supporting socio-economic activities but faces deterioration due to high traffic volumes, unpredictable weather, poor drainage, and inadequate maintenance. Traditional visual assessment methods are often time-consuming and subjective. In contrast, Geographic Information Systems (GIS) provide a more precise and efficient approach to road condition assessment, including drainage analysis. This study integrates Remote Sensing and GIS to develop an innovative virtual road condition assessment framework that combines pavement distress evaluation with drainage analysis. The research was conducted on selected roads within Jomo Kenyatta University of Agriculture and Technology (JKUAT). Using high-resolution drone imagery, field surveys, and GIS-based analysis, road conditions were assessed through pavement distress mapping, flow accumulation, curvature analysis, and road attribute evaluation. The results revealed that Innovation Street exhibited the most severe distresses, while Technology Street had predominantly minor to moderate deterioration. Commonly identified distresses included rutting, potholes, longitudinal and transverse cracking, weathering, and alligator cracking. The Quantum Pavement Condition Index (QPCI) effectively identified distress hotspots requiring urgent maintenance, demonstrating the framework’s potential to enhance road maintenance planning and decision-making. This study highlights the value of integrating GIS and remote sensing for efficient, data-driven infrastructure management, offering a scalable and resource-efficient approach for improving road maintenance strategies.