CivicLens AI

Inspiration

Across cities and communities, citizens regularly encounter issues such as potholes, garbage accumulation, water leakage, damaged streetlights, and unsafe public spaces.

While reporting systems exist, many complaints remain unresolved due to poor categorization, lack of prioritization, and fragmented communication.

We wanted to build a platform that makes civic reporting smarter by allowing AI to understand complaints, analyze evidence, and generate actionable insights for local authorities.

Using Google Gemini and Google Cloud, we created a system that helps communities and governments work together more efficiently.

What it does

Smart Issue Reporting

Citizens can report issues using text, images, or voice.

Image-Based Problem Detection

Upload a photo of a pothole, broken streetlight, or waste accumulation and receive automatic classification.

AI Complaint Summarization

Converts lengthy reports into concise summaries.

Priority Scoring

Automatically determines urgency based on severity and impact.

Location-Based Mapping

Displays reported issues on an interactive map.

Multilingual Support

Allows users to report issues in multiple languages.

Analytics Dashboard

Provides authorities with trends, hotspots, and resolution metrics.

How we built it

We built CivicLens AI as a cloud-native platform focused on improving communication between citizens and local authorities.

The frontend was developed using React.js and Tailwind CSS to create an intuitive interface for reporting and tracking civic issues.

The backend was built using Node.js and Express to manage reports, process media uploads, and communicate with Google Gemini.

Google Gemini powers the intelligence layer of the platform. It analyzes uploaded images, understands complaint descriptions, generates summaries, categorizes issues, and recommends priority levels.

All reports are securely stored and managed using Google Cloud services, ensuring scalability and reliability.

Throughout development, we focused on improving classification accuracy and creating meaningful insights for decision-makers.

Challenges we ran into

Diverse Complaint Types

Citizens report issues in many different formats and levels of detail.

Image Interpretation

Similar-looking images can represent very different civic problems.

Prioritization Logic

Determining urgency fairly and accurately required extensive testing.

Data Visualization

Presenting large numbers of reports in an understandable way was challenging.

Accomplishments that we're proud of

1. Built a complete AI-powered civic reporting platform.

2. Successfully integrated multimodal capabilities using Google Gemini.

3. Developed automated complaint categorization.

4. Created intelligent issue prioritization.

5. Built a scalable cloud-native architecture.

6. Improved accessibility through multilingual support.

What we learned

• Building multimodal AI applications.
• Geospatial data visualization.
• Prompt engineering for public-sector use cases.
• Cloud-native deployment on Google Cloud.
• Designing AI systems for social impact.

What's next for CivicLens AI

Predictive Infrastructure Monitoring

Identify areas likely to develop issues.

Government Integration

Connect directly with municipal service systems.

Community Voting

Allow citizens to vote on issue priorities.

Real-Time Notifications

Provide updates on issue resolution progress.

AI Resource Allocation

Help authorities optimize maintenance schedules.

Built With

• api
• authentication
• cloud
• css
• express.js
• firebase
• firestore
• gemini
• google
• maps
• node.js
• platform
• react.js
• rest
• run
• storage
• tailwind
• typescript