EnviroCast

Inspiration

Climate change is accelerating faster than traditional forecasting tools can keep up with. We were inspired by the idea of combining NASA’s real-time satellite data with the emerging power of quantum computing to create a tool that doesn’t just observe environmental change—it projects the future and empowers people, cities, and policymakers to shape it. We wanted a platform where anyone could explore the planet, understand how their city is doing, and see how today’s actions influence tomorrow’s world.

What it does

EnviroCast is an interactive, AI-powered environmental prediction platform.

It allows users to:

• Search any city, instantly fly to it on a 3D interactive Earth with smooth transitions.
• View detailed analytics using NASA satellite data: air quality, weather, pollution, deforestation, water levels, heat islands, and more.
• Use quantum-optimized forecasting models to simulate future environmental outcomes.
• Adjust environmental variables (emissions, renewable energy usage, conservation actions) and see instant changes in predicted futures.
• Access insights from multiple specialized AIs, each trained for environmental forecasting, climate modeling, or sustainability recommendations.

EnviroCast doesn’t just show data—it shows what’s possible.

How we built it

• Integrated live datasets from NASA Earth Observations (NEO), NASA Climate APIs, and global weather systems.
• Processed raw satellite data using a pipeline that combines classical ML with quantum-inspired optimization.
• Built a 3D Earth using WebGL/Three.js for instant city-level navigation.
• Developed multiple AI agents.
• Used quantum computing (or a cloud-based quantum simulator) to accelerate environmental prediction tasks.
• Built a fast, responsive Next.js front-end that allows real-time interaction with global data.

Challenges we ran into

• Normalizing and merging massive, high-resolution NASA datasets from different satellites.
• Achieving the right balance of quantum computation + classical ML to ensure speed and accuracy.
• Optimizing the 3D globe to be smooth, responsive, and data-rich without sacrificing performance.
• Creating a simple user experience for inherently complex environmental analytics.

Accomplishments that we're proud of

• Successfully combined real-time satellite data with an interactive 3D globe.
• Built one of the first accessible platforms using quantum-enhanced environmental forecasting.
• Enabled users to tweak environmental variables and instantly visualize how the planet’s future changes.
• Developed a collaborative multi-AI system that provides deep and accurate sustainability insights.

What we learned

• The challenges and rewards of working with very large scientific datasets.
• How quantum computing can boost environmental modeling when used strategically.
• The importance of making climate analytics understandable and actionable.
• How to design intuitive interfaces for complex data-driven predictions.

What's next for EnviroCast

• Introducing hyper-local environmental predictions on a neighborhood scale.
• Adding carbon tracking for individuals, cities, and organizations.
• Partnering with environmental groups, climate labs, and government agencies.
• Creating a mobile app version of EnviroCast.
• Integrating more advanced quantum models as the hardware evolves.

Built With

• css
• framer-motion
• github
• grok
• html
• javascript
• llama
• nasa
• nextjs
• react
• satelitedata
• shadcn
• streamlit
• tailwindcss
• three.js
• webgl-earth