Smart City Vision: A Geo-tagged AI System of Automated Road damage detection and civic workflow management

The municipal grievance systems have been found to have some difficulties in delayed response, manual verification and non-transparency which have impeded proper management of the infrastructure. Conventional channels are dependent on written descriptions and manual inspection, which make them ineffective and unstable in terms of decision-making. In order to eliminate these constraints, this study presents a Smart City Civic Intelligence and Resolution Management Platform that combines artificial intelligence with web-based processes. Constructed on the basis of Django, the system enables citizens to report complaints with an option to upload the images, which are processed with the help of YOLOv8 to identify the damage which is evident and is then used to classify the severity and estimate the cost of repair. Performance on testing shows that the prototype has acceptable levels of accuracy with inference time of between 1-3 seconds on each image, which illustrates that the system can help revolutionize the municipal complaint management system. The paper sets the stage of scalable smart city governance and the future goals encompass regression-based cost forecast, GIS amalgamation, and implementation of a mobile application.