Work together with your community to snowball small changes to global impact.
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Table of Contents

1. About The Project
   • Built With
2. Get Started
3. License
4. Contact
5. Acknowledgments

About The Project

Imagine your neighborhood. Small environmental issues—litter, illegal dumping, mismanaged waste—often go unnoticed or unaddressed. Yet these minor problems can quickly snowball, deteriorating local quality of life and contributing to broader issues like urban decay and pollution. Research shows that even a slight uptick in household waste mismanagement can trigger significant environmental damage. For instance, the World Bank’s What a Waste 2.0 report projects global waste to surge from 2.01 billion tonnes in 2016 to 3.40 billion tonnes by 2050, with at least 33% mismanaged.

Our solution is simple: a community-driven platform that empowers residents to capture and share these issues with just a click. Snap a photo, add a quick note, and let our AI do the heavy lifting—identifying critical problems, categorizing them, and even initiating polls that automatically connect you with local authorities.

How it works

Intelligent Reporting & Analysis: Residents capture a photo of a local issue with a title, and the platform auto-captures location data while an AI model recognizes the object. The combined details are then processed by a generative model that suggests a detailed description, relevant tags, and a severity assessment to prioritize action.

Real-Time Mapping & Automation: Reported issues are instantly aggregated on an interactive map, highlighting neighborhood hotspots. When community polls indicate critical concern, the system automatically connects with the appropriate authorities and initiates a call on behalf of the users.

Technology

Frontend: Built with Next.js/React and TailwindCSS for a smooth and responsive user experience.

Backend: Powered by FastAPI and NextServer, handling requests efficiently.

Database: Supabase for effecient querying, and scaling.

AI / ML: Used a transfer-learned YOLO v11 model fine-tuned on a 40k-image dataset (including light posts, floods, potholes, and litter) using Roboflow, with OpenCV on Modal for real-time image processing. Integrations with FastAPI, Hume, Twilio, and Anthropic API drive intelligent analysis and automated workflows.

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Built With

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Get started

Here are the steps to run the project locally if you want to develop your own project.

Prerequisites

• npm

  cd frontend
  npm install npm@latest -g

Run Frontend

You can run the project using

npm run server

License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Project Link: https://github.com/ishmamf/envolve

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Acknowledgments

This project was submitted to HackKnight Spring 2025 hackathon for the Environment track.

Devpost link: https://devpost.com/software/envolve-eunj39

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