An AI and GIS-Based Framework for Pothole Severity Assessment, Repair Priority Ranking, and Cost Prediction

Potholes are a major form of road deterioration that can affect road safety, vehicle performance, traffic movement, and transportation infrastructure. Traditional road inspection mainly depends on manual surveys and complaint-based reporting, which can be time-consuming, costly, and difficult to apply across large road networks. This paper proposes an integrated Artificial Intelligence (AI) and Geographic Information System (GIS)-based framework for automated pothole detection, severity assessment, repair-priority ranking, and approximate cost prediction. The framework uses YOLO-based computer vision to detect potholes from road images and extract visual characteristics including bounding-box width, height, area, aspect ratio, and relative image coverage. These features are intended for machine-learning models such as Random Forest and XGBoost to classify potholes into Low, Medium, and High severity categories. GIS-based analysis then incorporates contextual information such as road class, traffic conditions, proximity to critical services, and pothole distribution. Multi-Criteria Decision Analysis (MCDA) combines severity and spatial risk factors to generate repair-priority rankings. Regression models are proposed for approximate repair-cost prediction using defect characteristics, repair category, and road type. The current cleaned dataset contains 1,740 pothole records with image and bounding-box information. Because actual repair-cost labels are not present in the current dataset, cost prediction is defined as a subsequent stage requiring supplementary maintenance-cost records. The main contribution is a unified decision-support framework connecting visual detection, severity assessment, geographic context, repair prioritization, and cost planning.