CinderSight: Wildfire Prediction with AI

Wildfires result in the emission of nearly 3.3 billion tonnes of CO2 annually and cost the U.S. economy an estimated $70 billion. With a steady increase in wildfires worldwide, emergency responders and wildfire management agencies face significant challenges in allocating resources to mitigate environmental and economic damage. CinderSight is a state-of-the-art wildfire spread prediction system that enhances wildfire management strategies by leveraging advanced deep learning techniques. We aim to deliver precise and timely predictions on wildfire movement to aid in effective response and mitigation. Through CinderSight, we hope to empower emergency responders and wildfire management agencies to mitigate the threat to our ecosystems, infrastructure, and lives.

Explore our site for an in-depth look at what powers our model!

Wildfire Risk

Forest Service - U.S. Department of Agriculture

Map of fire risk

Project Objectives

Our aims for CinderSight

Enhance Accuracy

Integrate enriched multimodal data (satellite imagery, weather forecasts, terrain, and human factors) to boost wildfire spread forecasting performance.

Innovative Architecture

Design and implement deep learning models (CNNs, ConvLSTMs, Transformers) tailored for real-time wildfire forecasting.

Interpretability

Provide clear, actionable insights so decision-makers and responders can trust and act on predictions.