Inspiration
Road damage, potholes, and broken streetlights affect people's daily lives, yet reporting these problems and tracking their resolution can be difficult. We wanted to explore how technology could make civic reporting more organised, accessible, and transparent.
What it does
CivicLens is an AI-assisted civic reporting platform that helps citizens report infrastructure issues, view them on an interactive map, identify potentially duplicate reports, and prioritise problems based on severity and public impact.
How we plan to build it
We plan to use HTML, CSS, and JavaScript for the frontend, Python Flask for the backend, and SQLite for data storage. Leaflet and OpenStreetMap will help display reported issues geographically. Rule-based prioritisation and duplicate detection will form the initial prototype.
Challenges we anticipate
Key challenges include accurately identifying duplicate reports, assigning meaningful priority scores, and displaying location data clearly. We plan to address these challenges through simple, testable methods and a focused prototype.
Accomplishments we aim for
Our goal is to build a working prototype that demonstrates the complete reporting process, displays civic issues on a map, and helps users understand which reported problems remain unresolved.
What we aim to learn
We want to gain practical experience with backend development, mapping APIs, data management, and AI-assisted problem-solving while building a solution to a real-world civic challenge.
What's next for CivicLens
Future improvements could include image-based road damage detection, more advanced duplicate identification, and integration with official civic reporting systems where supported.
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