iWatchRoadv2: Pothole Detection, Geospatial Mapping, and Intelligent Road Governance

2025-10-18

AI Summary

To address safety and management challenges arising from frequent road potholes and delayed maintenance in India, this paper proposes an end-to-end intelligent road asset management system. Methodologically, it employs a fine-tuned Ultralytics YOLO model for high-accuracy pothole detection; integrates OCR with external GPS logs for spatiotemporal synchronization and geotagging; constructs a self-annotated dataset comprising over 7,000 frames of Indian road scenes; leverages OpenStreetMap for real-time infrastructure health visualization; and links road segments to contractor contracts via secure authentication, automatically triggering contractor alerts and warranty responses. Key contributions include: (1) the first integration of detection, precise localization, accountability assignment, and repair closure within a lightweight, deployable platform; (2) support for citizen participation and scalable deployment across urban and rural settings; and (3) empirical validation of geospatially aware AI governance for infrastructure operations—demonstrating both feasibility and operational efficacy.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance

Abstract

Road potholes pose significant safety hazards and maintenance challenges, particularly on India's diverse and under-maintained road networks. This paper presents iWatchRoadv2, a fully automated end-to-end platform for real-time pothole detection, GPS-based geotagging, and dynamic road health visualization using OpenStreetMap (OSM). We curated a self-annotated dataset of over 7,000 dashcam frames capturing diverse Indian road conditions, weather patterns, and lighting scenarios, which we used to fine-tune the Ultralytics YOLO model for accurate pothole detection. The system synchronizes OCR-extracted video timestamps with external GPS logs to precisely geolocate each detected pothole, enriching detections with comprehensive metadata, including road segment attribution and contractor information managed through an optimized backend database. iWatchRoadv2 introduces intelligent governance features that enable authorities to link road segments with contract metadata through a secure login interface. The system automatically sends alerts to contractors and officials when road health deteriorates, supporting automated accountability and warranty enforcement. The intuitive web interface delivers actionable analytics to stakeholders and the public, facilitating evidence-driven repair planning, budget allocation, and quality assessment. Our cost-effective and scalable solution streamlines frame processing and storage while supporting seamless public engagement for urban and rural deployments. By automating the complete pothole monitoring lifecycle, from detection to repair verification, iWatchRoadv2 enables data-driven smart city management, transparent governance, and sustainable improvements in road infrastructure maintenance. The platform and live demonstration are accessible at https://smlab.niser.ac.in/project/iwatchroad.

Problem

• Automated pothole detection on diverse Indian roads
• Geospatial mapping and real-time road health monitoring
• Enabling data-driven governance and automated repair accountability

Innovation

• Fine-tuned YOLO model for pothole detection
• Synchronized GPS and OCR for precise geolocation
• Automated alerts and governance via web interface