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Objective: Design and develop a mobile application or web-based dashboard for the real-time visualization of Network Survey Vehicle (NSV) data during site inspections and for remote access by engineers, auditors, or NHAI officials. 🧠 Prompt to Build the Project Build an AI- and GIS-enabled mobile/web application that can: Ingest NSV pavement condition data (including geo-stamped distress metrics like roughness, rutting, ravelling, and cracking). Play synchronized dashboard camera footage alongside the map to match the road segment being inspected. Display interactive, lane-wise maps with color-coded pavement conditions using GPS coordinates. Allow filtering by distress type, severity level, road ID, date, or segment length. Show charts or trend visualizations of pavement health across time and routes. Include a summary dashboard showing statistics like: % of road network with critical damage Top 5 damaged segments Pavement Health Index per route Enable report export (PDF/CSV) and real-time notes or tagging by field inspectors. Be optimized for use on mobile phones or tablets by highway engineers in the field. ⚙️ Technology Stack Suggestions: Frontend: React Native or Flutter (for mobile); React.js or Vue.js (for web) Mapping/GIS: Leaflet.js / Mapbox / Google Maps API Backend: Node.js or Python (Flask/Django) Database: PostgreSQL with PostGIS for spatial queries Video Syncing: HTML5 video + timeline mapping logic Optional: Machine Learning for auto-identification of damage segments from video (advanced) 📥 Input Data: Geo-stamped CSV reports (with latitude, longitude, distress type, severity, etc.) Dashboard video footage (MP4 format) 📤 Expected Deliverables: Fully functional prototype or demo app (APK or live link) Source code (well-documented) Concept note / documentation describing: Architecture Data flow Visualization logic Tools/libraries used Presentation deck for demo day (PDF/Slides) 🎯 Goals: Help NHAI engineers view and assess NSV reports in real-time in an intuitive and efficient manner. Reduce manual effort in identifying road distress during inspection. Improve response time and data interpretation for remote decision-making. 🏆 Optimize for: Innovation (UI, visualization techniques, video-data integration) Feasibility (working MVP within 7 days) Scalability (can scale to pan-India highway data) User Experience (field usability, offline mode optional) 🔗 Sample Data Reference: https://drive.google.com/drive/folders/1TZ8eVn24W-iLkBIlFR3O9PSH4mnnd8w?usp=sharing 📣 Bonus Tips for Your Hackathon Team Assign roles: Backend, frontend, data integration, presentation. Start with basic map + data point plotting → then sync with video. Keep UI clean, intuitive, mobile-friendly. Highlight unique features in your submission deck (e.g., AI tagging, heatmaps, etc.). Include a short demo video if possible during submission. - Initial Deployment
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