Pothole Patrol

Inspiration

Navigating through a maze of pothole-ridden roads inspired us to create Pothole Patrol. We envisioned a solution that empowers individuals to actively contribute to safer streets by identifying and reporting road hazards with ease.

What it does

Pothole Patrol is your go-to app for tackling road imperfections head-on. Capture and submit pothole images, along with user and pothole information. The app also offers a comprehensive map displaying pothole locations, their status, and an interactive HeatMap for concentrated areas. Users can climb the ranks on the leaderboard by their engagement, while real-time status updates keep everyone informed.

How we built it

We utilized Visual Studio Code and used a pre-built full stack template as a basis for our project. We implemented Mapbox API for a heatmap, react/JavaScript to create input forms and a leadership board, PyTorch for machine learning to verify pothole pictures, and postgresSQL for the backend database.

Challenges we ran into

Creating a seamless experience presented its challenges. Integrating data validation while maintaining user privacy was a balancing act. Ensuring distance accuracy required innovative problem-solving. Overcoming these hurdles underscored our commitment to delivering a trustworthy app.

Accomplishments that we're proud of

Pothole Patrol's user leaderboard fosters community engagement, aligning with our vision of collaborative road care. Validating pothole data and implementing photo verification boost app credibility. We're especially proud of our interactive HeatMap feature, providing insights to local authorities for strategic road maintenance.

What we learned

There were a lot of things we forgot from bootcamp
How to create a file input
How to send data to the backend without using Saga
Potholes aren't actually holes
Potholes can't hurt you if you are not scared of them

What's next for Pothole Tracker

Expand beyond potholes
Add authentication and authorization process
Create Admin Access for City Staffers to Resolve and Update Pothole information
Increase scope to more than San Jose and make global
Mobile version

Built With

Submitted to

FUN HACKATHON WEEKEND: Make the world a better place (Hacker Dojo)
- Winner Mystery prize

Created by

I help managed and guided a team of 9 toward the target of achieving 1st place. I planned out the project outline, and facilitated the talks between members of different expertise, and introduce a MapBox GL open-source project as a tech-stack basis to simplify building and provide foundation for our app. I keep track of deliverables and help assign team members into different task that are best tied to their skillets. Toward the end, I help create the final presentation video and slides as required for final submission.

Ed Wu
I worked on the frontend utilizing a project template to speed up SLDC process. I also leveraged React Router/React/CSS to create a one-page application along with creating an input form for users to send data to the backend. Also helped with setting up audio recording and assistance in building the Google Slide presentation.

YuhBoh Feng
I built the 3D heatmap with Mapbox and react. A circle layer is visible with high zoom levels. Circles increase in size and become more red the more severe the pothole is. Zooming out further, a heatmap layer is exposed, where more severe potholes are weighted more. Color and intensity increase with the heatmap's density

Jacob Asper
I built the backend golang based service, provided the restapis to persist the data in postgresDB. Which other services would use to track the pothole status in terms of how it is progressing towards not being a pothole. Clear the data from database once the pothole is taken care of.

Naveen Gunukula
I was responsible for creating the schemas and shaping the data responses from the API for the frontend, designing and styling the sidebar, and creating CSVs using node.js.

Tim Engle
I built the machine learning algorithm used to validate the uploaded image to verify if the image provided contained a pothole or not. I used PyTorch framework to built a CNN based Image Classification model and built an API using flask framework in order to integrate it into the web application.

Shoumik Nandi
Patrick Daly
Emily Ann Ramos
Civic Tech Advocate