Inspiration
In our cities, countless damages—like broken sidewalks, potholes, and damaged infrastructure—pose risks to public safety and diminish the quality of life that comes with a clean, well-maintained environment. Yet, identifying and addressing every issue can be challenging for government officials without direct input from the community.
What it does
Our application empowers citizens to take an active role in maintaining their communities. When users encounter damages throughout the city, they can capture a photo, record the location and severity, and submit it directly through the app. Using machine learning, the system automatically categorizes each issue and visualizes them on an interactive map, helping authorities prioritize and respond more efficiently.
How we built it
We used the Next.js framework to build the frontend application, Express.js to build the backend API that dealt with submitting reports, and Python to build our ML API that categorizes the the images submitted through our reports. Our ML API consists of a Flask server and uses Gemini to categorize images of the damages submitted.
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