EcoPrior AI

EcoPrior AI — Environmental Restoration Intelligence

Environmental restoration resources are limited, but ecological problems are not evenly distributed. EcoPrior AI was created to answer a simple question: where should restoration efforts be prioritized first, and why?

EcoPrior AI is an AI-powered environmental decision-support prototype that combines environmental telemetry with transparent scoring to identify restoration priorities across Indian states.

The platform analyzes four environmental signals:

• Thermal stress
• Hydrologic stress
• Active fire and thermal activity
• Vegetation health

These signals are combined into a transparent 0–100 EcoPrior Score, allowing regions to be categorized into Low, Moderate, High, or Critical restoration priority.

The platform provides an interactive map where users can explore different regions and understand the environmental factors contributing to their priority score.

The AI Copilot is powered by Google Gemini. It receives the selected area's environmental profile and answers questions based on the available environmental data. The system separates observed data from AI interpretation and potential conservation actions, while explicitly identifying unavailable signals.

Inspiration I was inspired by the challenge of deciding where limited environmental restoration resources should be directed. Instead of simply displaying environmental data, we wanted to build a system that could transform multiple environmental signals into an understandable restoration-prioritization framework.

How I built it

EcoPrior AI was developed using Google AI Studio and exported for local development and deployment.

The application combines a React and TypeScript interface, interactive geospatial mapping, environmental data services, a transparent scoring engine, and a Gemini-powered AI Copilot.

The scoring engine uses weighted environmental signals and dynamically handles unavailable data rather than treating missing information as zero environmental stress.

What I learned

We learned how to combine multiple environmental data sources, build an explainable scoring system, integrate Gemini into an application, work with geospatial visualization, and design AI responses that distinguish observed measurements from interpretation.

Challenges

A major challenge was handling incomplete environmental telemetry. Not every signal is always available, so EcoPrior AI explicitly identifies missing data and adjusts the scoring calculation accordingly.

Another challenge was taking an application built in Google AI Studio and adapting it for external deployment while keeping API credentials secure.

EcoPrior AI is a prototype decision-support system. Its scores and recommendations should be validated using field observations and qualified environmental professionals.

Built With

• ai
• artificial
• environmental
• express.js
• firms
• gemini
• google
• intelligence
• leaflet.js
• nasa
• node.js
• open-meteo
• react
• studio
• typescript
• vite