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

• URL: http://arxiv.org/abs/2510.16375v1
• Date: Sat, 18 Oct 2025 07:11:03 GMT
• Title: iWatchRoadv2: Pothole Detection, Geospatial Mapping, and Intelligent Road Governance
• Authors: Rishi Raj Sahoo, Surbhi Saswati Mohanty, Subhankar Mishra,
• Abstract summary: iWatchRoadv2 is an end-to-end platform for real-time pothole detection, GPS-based geotagging, and dynamic road health visualization.<n>We curated a self-annotated dataset of over 7,000 dashcam frames capturing diverse Indian road conditions.<n>The system synchronizes OCR-extracted video timestamps with external GPS logs to precisely geolocate each detected pothole.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• iWatchRoad: Scalable Detection and Geospatial Visualization of Potholes for Smart Cities [0.0]
  We present a complete end-to-end system called iWatchRoad for automated pothole detection, GPS tagging, and real time mapping.<n>We curated a dataset of over 7,000 frames captured across various road types, lighting conditions, and weather scenarios.<n> timestamps are synchronized with GPS logs to geotag each detected potholes accurately.
  arXiv  Detail & Related papers  (2025-08-13T15:26:03Z)
• Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems [0.0]
  Road anomaly detection plays a crucial role in road maintenance and in enhancing the safety of both drivers and vehicles.<n>Recent machine learning approaches for road anomaly detection have overcome the tedious and time-consuming process of manual analysis and anomaly counting.<n>In this paper, we leverage transfer learning by adopting a pre-trained YOLOv8-seg model for the automatic characterization of potholes.
  arXiv  Detail & Related papers  (2025-04-18T11:59:38Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Your Car Tells Me Where You Drove: A Novel Path Inference Attack via CAN Bus and OBD-II Data [57.22545280370174]
  On Path Diagnostic - Intrusion & Inference (OPD-II) is a novel path inference attack leveraging a physical car model and a map matching algorithm. We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios.
  arXiv  Detail & Related papers  (2024-06-30T04:21:46Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view.<n>To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles [58.461077944514564]
  This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles. MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction. Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
  arXiv  Detail & Related papers  (2023-10-08T21:32:30Z)
• RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving [67.09546127265034]
  Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems. We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions. It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
  arXiv  Detail & Related papers  (2023-10-03T17:59:32Z)
• OASIS: Automated Assessment of Urban Pedestrian Paths at Scale [16.675093530600154]
  We develop a free and open-source automated mapping system to extract sidewalk network data using mobile physical devices. We describe a prototype system trained and tested with imagery collected in real-world settings, alongside human surveyors who are part of the local transit pathway review team.
  arXiv  Detail & Related papers  (2023-03-04T01:32:59Z)
• Haul Road Mapping from GPS Traces [0.0]
  This paper investigates the possibility of automatically deriving an accurate representation of the road network using GPS data available from haul trucks operating on site. Based on shortcomings seen in all tested algorithms, a post-processing step is developed which geometrically analyses the created road map for artefacts typical of free-drive areas on mine sites.
  arXiv  Detail & Related papers  (2022-06-27T04:35:06Z)
• RoadAtlas: Intelligent Platform for Automated Road Defect Detection and Asset Management [8.441428233111843]
  RoadAtlas is a novel end-to-end integrated system that can support road defect detection, road marking parsing, and a web-based dashboard for presenting and inputting data by users.
  arXiv  Detail & Related papers  (2021-09-08T01:26:15Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.