FireGuard XR

Detecting wildfires early and predicting their spread with AI, satellite data, and real-time environmental monitoring.

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Inspiration

Wildfires are becoming one of the most destructive natural disasters worldwide, especially in forested regions where fires can spread rapidly and threaten communities, wildlife, and infrastructure. In many cases, wildfires grow out of control because they are detected too late or because responders lack real-time information about how the fire is spreading.

We were inspired to create FireGuard XR as a proactive wildfire intelligence system that focuses on early detection, predictive modeling, and real-time situational awareness. Our goal was to combine satellite imagery, environmental data, and spatial visualization to help detect fires sooner and provide actionable insights to emergency responders and nearby communities.

What it does

FireGuard XR is an AI-powered wildfire detection and prediction platform that monitors satellite imagery and environmental conditions in real time.

The system continuously scans satellite images for signs of wildfire activity. If a potential fire is detected, the platform increases scanning frequency and analyzes additional environmental data to confirm the event.

Once a wildfire is verified, FireGuard XR predicts how the fire may spread based on environmental factors such as temperature, humidity, and wind conditions.

The platform then visualizes this prediction on an interactive 3D map, allowing users to see the estimated wildfire spread over time. The system can also alert nearby fire stations and provide situational awareness to the public.

Additional features include:

Real-time wildfire detection from satellite imagery

A 3D wildfire spread simulation map

Environmental monitoring (temperature, humidity, wind speed, air quality)

A 7-day weather and wildfire risk forecast

GPS-based user location tracking

Emergency alerts and evacuation awareness

Live wildfire-related news updates

Together, these features create a centralized platform for monitoring wildfire risks and responding more quickly.

How we built it

FireGuard XR combines modern web technologies, AI-powered image analysis, and real-time geospatial data.

The frontend is built using React, TypeScript, and Vite, providing a fast and responsive user interface. We use Three.js to render the interactive 3D wildfire spread visualization, allowing users to observe how fires may expand over time.

For location services and map interaction, the platform integrates the Google Maps API, enabling location search, GPS tracking, and geographic context.

The backend is powered by FastAPI (Python), which handles API requests, processes environmental data, and manages wildfire detection workflows.

Satellite imagery is periodically retrieved from satellite APIs and processed through Cloudinary’s media processing tools, which help analyze image data for potential wildfire indicators.

Real-time environmental data such as temperature, humidity, wind speed, and air quality are fetched through weather APIs and used to calculate wildfire risk and prediction models.

The infrastructure is hosted on Vultr Cloud, while secure network connectivity for development and services is handled through Tailscale. Authentication and secure access are managed using Auth0, and ElevenLabs is used to generate AI voice alerts for emergency notifications.

The final system combines these technologies into a unified dashboard that continuously monitors wildfire conditions.

Challenges we ran into

One of the biggest challenges was integrating multiple real-time data sources into a single cohesive system. Satellite imagery, weather data, environmental metrics, and geographic information all needed to work together seamlessly.

Another challenge was designing a system capable of detecting potential wildfire activity without producing false positives. We needed to carefully structure the detection workflow so that the platform could escalate scanning frequency and verify events before triggering alerts.

Building a responsive 3D visualization for wildfire spread was also technically complex. Rendering environmental simulations while keeping the interface fast and interactive required careful optimization.

Finally, coordinating multiple APIs and ensuring that all data remained accurate and location-aware added additional complexity during development.

Accomplishments that we're proud of

We are proud to have built a working prototype of a system that combines satellite monitoring, environmental analytics, and 3D visualization into a single platform.

Some of the accomplishments we’re most proud of include:

Implementing a satellite imagery scanning pipeline

Creating a real-time wildfire detection workflow

Building an interactive 3D wildfire spread visualization

Integrating multiple external APIs for environmental data

Designing a unified wildfire monitoring dashboard

Most importantly, we created a platform that demonstrates how AI and spatial technologies can be used to improve disaster awareness and response.

What we learned

Through this project we learned how to integrate complex geospatial data into modern web applications. Working with satellite imagery, environmental datasets, and predictive modeling required us to explore new approaches to data visualization and real-time monitoring.

We also gained experience building scalable backend systems using FastAPI and managing cloud infrastructure for data processing workflows.

Additionally, the project helped us better understand how environmental factors such as wind, humidity, and temperature influence wildfire behavior.

What's next for FireGuard XR

FireGuard XR has the potential to evolve into a much more advanced wildfire intelligence platform.

Future improvements could include integrating drone-based wildfire monitoring, expanding predictive modeling for more accurate fire spread simulations, and developing mobile applications for emergency responders and local communities.

We would also like to integrate additional environmental sensors and real-time government wildfire datasets to improve detection accuracy and response speed.

Ultimately, our vision is for FireGuard XR to become a comprehensive wildfire monitoring system that helps communities prepare for and respond to wildfire threats more effectively.

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