The Problem is Wildfires Destroy Livelihoods

There exist many natural disasters in the world. Flooding, fires and tornadoes all cause huge widespread damage and suffering worldwide, affecting people and infrastructure. Today, in the 21st Century, we can predict rainfall and thus calculate the chances of flooding, we can monitor wind speeds and directions and thus predict hurricane paths. And yet, there seems to be very few resources for predicting wildfires, particularly with regard to using up and coming technologies like artificial intelligence and machine learning.

Our Solution is Predicting Wildfires Using Artificial Intelligence

We predict where outbreaks of wildfires are most likely to occur, based on hyperlocal weather data. Our prediction model is based on advanced machine learning techniques, giving an accuracy of 74.8%. Using our predictions, firefighters worldwide can look ahead and prevent wildfires before they happen.

App Features

Data point locations determined by satellite imagery

Forest coordinates autonomously determined by analysis of LandSat8 Imagery and used as the basis for the data point locations in AWARE.

Areas in Danger

Weekly average likelihood summarised in the Areas in Danger table, giving an overview of where in the world is most at risk of wildfires occurring.

Filter Data

Easily configure the app to show data points you're interested in the most - using the search bar, filter tool, and day select slider!

Detailed information about every data point

Detailed annotations give information about the nearest fire services to the selected data point

Prediction Center

Check out the Prediction Center, where you can find tabulated fire info and an option to export to CSV

Trend Graph

Use the trend graph to display a graphical summary of fire likelihood on each upcoming day of the week

Wind Layer

We've also got a wind layer, to demonstrate energy flows and how a fire will most likely spread

Settings Panel

Use the settings panel to modify your WorldWind experience! Change the globe resolution, satellite imagery, and the colour scheme being used.

Our Team

Peter Jupp

Lead Website Developer and Data Analyst

Vishal Soomaney

Lead Backend Developer and Senior Data Analyst

Flinn Dolman

Algorithm Design, Implementation and Web Developer

Our team is made up of Computer Science and Maths students from the University of York. Peter and Vishal are both in the placement year of their four year Computer Science degrees. Flinn graduated from the University of York with a degree in Mathematics. He has recently embarked on a Masters at University College London.

Our Journey

  • Birth of Wildfire AWARE

    June 2017

    University of York students Peter, Vishal and Flinn joined forces and began development of Wildfire AWARE, aiming to compete in the NASA Europa Challenge.

  • Participation in the NASA Europa Challenge Finals

    August 2017

    The team spent several days in Helsinki, Finland, competing in the NASA Europa Challenge Finals. Judged by a team of NASA, ESA and Finnish researchers and experts, Wildfire AWARE came in 4th place.

  • Awarded First Prize of the Smart Cities Entrepreneurship Competition

    August 2017

    AWARE was awarded First Prize in the SMARTIES Competition, a competition designed to promote innovate smart city applications, funded by the Research Councils UK.

  • The Future...

    2017 and Beyond

    Wildfire AWARE is still in development. We are constantly finding new ways to increase the accuracy of our predictions and improve the way we visualise this data on the web. The future is bright for AWARE!