MageroOduor/Predictive-Infrastructure-Maintenance-Using-ML-AI

Infrastructure deterioration is a major and costly challenge in many developing economies, particularly across African urban centres. Roads are continuously exposed to traffic pressure and environmental conditions, leading to gradual damage such as potholes. **Business Understanding** Infrastructure deterioration is a major and costly challenge in many developing economies, particularly across African urban centres. Roads are continuously exposed to traffic pressure and environmental conditions, leading to gradual damage such as potholes. If not identified early, these minor defects develop into major failures, increasing repair costs and posing safety risks to road users. Current maintenance practices are largely manual and reactive, relying on physical inspections or public complaints. This often results in nual inspection methods are labor-intensive, often reactive, and can lead to delayed repairs, increased road hazards, inefficient resource allocation, and increased operational costs. At the same time, deploying extensive monitoring infrastructure, such as fixed cameras across all roads, is not feasible due to financial and logistical constraints. There is therefore a need for a cost-effective and scalable approach that can automatically detect road damage and support timely maintenance decisions. Artificial Intelligence offers a practical solution by enabling models to analyze images and identify defects early. The ultimate goal is to move towards a proactive and data-driven approach to road maintenance. **Expected Outcomes** By developing and deploying a robust pothole segmentation model, we aim to achieve the following business outcomes and enable critical decisions: Optimized Resource Allocation: The model will provide precise location and severity (area, percentage of damage) of potholes, allowing road authorities to prioritize repairs effectively, leading to more efficient deployment of maintenance crews and budget. Proactive Maintenance Planning: Longitudinal data gathered through continuous monitoring will inform data-driven urban planning, enabling predictive maintenance strategies and the design of more resilient road infrastructure. Reduced Costs: By identifying and addressing smaller potholes befo …