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Develop AI-Powered Pothole Reporting and Tracking Platform

Description

Citizens in Hyderabad frequently encounter potholes that pose safety risks and damage vehicles. Existing complaint systems often lack proper tracking, prioritization, and transparency, causing citizens to lose confidence in the reporting process.

We need to develop a CivicTech solution that enables citizens to report potholes easily and allows authorities to efficiently manage and resolve complaints.

The platform should use Artificial Intelligence to detect potholes from uploaded images, classify their severity, automatically capture GPS coordinates, and provide complaint tracking until resolution.

Objectives

  • Simplify pothole reporting for citizens.
  • Automatically detect potholes using AI.
  • Classify pothole severity levels.
  • Capture GPS location automatically.
  • Provide complaint tracking and status updates.
  • Generate analytics for authorities.

User Story

As a citizen,

I want to upload a pothole image and location,

So that I can quickly report road issues and track their resolution status.

Features

Citizen Module

  • Upload pothole image
  • Auto GPS detection
  • Manual location entry (fallback)
  • Complaint submission
  • Complaint tracking using Complaint ID

AI Module

  • Detect potholes from uploaded images

  • Confidence score generation

  • Severity classification:

    • Low
    • Medium
    • High
    • Critical

Dashboard Module

  • View reported potholes
  • Interactive map visualization
  • Complaint statistics
  • Area-wise reports

Admin Module

  • Review complaints
  • Assign complaints
  • Update status
  • View analytics dashboard

Complaint Workflow

  1. Citizen uploads image
  2. AI analyzes image
  3. Severity is determined
  4. GPS coordinates captured
  5. Complaint generated
  6. Complaint assigned to authority
  7. Status updated
  8. Citizen tracks progress

Expected Deliverables

  • Responsive web application
  • AI pothole detection model
  • Complaint tracking system
  • Interactive map dashboard
  • Admin management panel

Tech Stack

Frontend

  • React.js
  • Tailwind CSS

Backend

  • Python
  • FastAPI

Database

  • PostgreSQL

AI/ML

  • YOLOv8
  • OpenCV

Maps

  • Leaflet.js
  • OpenStreetMap

Acceptance Criteria

  • Users can upload pothole images successfully.
  • AI detects potholes with confidence scores.
  • GPS location is captured accurately.
  • Complaint IDs are generated.
  • Users can track complaint status.
  • Dashboard displays real-time complaint statistics.
  • Application is responsive across devices.

Priority

High

Labels

CivicTech, AI, MachineLearning, ComputerVision, React, Python, Hackathon

Estimated Timeline

Hackathon MVP: 24–48 Hours