Smart Pothole Detection and Geospatial Visualization using Deep Learning and ArcGIS

This paper presents a complete system that combines artificial intelligence and geospatial analysis for pothole detection and demonstration on roads. Overcoming the inefficiencies of manual inspection of delay, time-consuming, and susceptible to human error, the system combines deep learning algorithms and ArcGIS to effectively detect potholes. Deep networks like VGG16, ResNet50, and Mask R-CNN are used in the model to enable accurate pothole detection on road surfaces. Moreover, integration with ArcGIS enables spatial mapping for better maintenance planning, while web interface with GPS support enables citizen engagement through real-time pothole reporting. The system performed quite well with an accuracy of 93% and F1 score of 95.86%. The Mask R-CNN model achieved 73% precision much higher than baseline models like YOLOv3. Results vindicate the system's potential as a viable, scalable, and community-based device for road monitoring and infrastructure maintenance.