AeroNav AI

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  Streamlit interface with Gemini Insight

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  the Plotly Image of Eco-optimized vs standard flight route from Tokyo to London showing fuel and CO₂ savings

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  The Pipeline of Our Project

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Inspiration

Growing aviation emissions and the lack of accessible tools for eco-efficient flight planning inspired us to build a smarter, sustainability-focused routing system.

What it does

AeroNav AI analyzes flight routes to optimize fuel usage and reduce CO₂ emissions while providing visual dashboards and AI-generated insights.

How we built it

We used Python, data models, route-optimization logic, Plotly for visualization, the Gemini API for insights, and MongoDB Atlas for storage.

Challenges we ran into

Simulating realistic wind and routing Balancing fuel vs time trade-offs Integrating AI insights smoothly Managing data flow across components

Accomplishments that we're proud of

End-to-end working system (input → analysis → visualization → storage) Interactive route maps and dashboards AI-powered flight insights Clean and structured architecture

What we learned

Real-world trade-offs in aviation optimization Integrating AI with data pipelines Building full-stack data-driven applications Importance of visualization for decision-making

What's next for AeroNav AI

Integrate real-time weather and wind data Improve fuel and aircraft performance models Deploy as a scalable web application Add ML-based route optimization

Built With

• api
• gemini
• json
• jupyternotebook
• mongodb
• numbpy
• plotly
• python