Throughout 2020, there have been multiple devastating wildfires all around the world. Headlines filled our newsfeeds, ranging from infernos racing their way across Australia to gender reveal parties sparking flames in California. We knew that there needed to be a better way of managing how wildfires are detected. Our shared passion for environmentalism, coupled with our emerging skills in Python and interests in artificial intelligence, inspired us to take the first steps to a cleaner future. We decided that to solve the problem, it would be essential to detect wildfires as early as possible, and in turn, to respond to them quickly.

What it does

Wildlife watch starts with OpenCV reading frames from a deployed drone video feed. The video feed is continually passed into Google’s vision AI, which returns a list of detected phenomena with associated probabilities. If our pipeline detects a high chance of a wildfire, the frame is saved and the instance’s current geolocation is stored. This information can be used to alert relevant authorities.

How we built it

Wildfire Watch was created using Python, OpenCV, Google Cloud, and Google’s Vision API.

Challenges we ran into

It was all of our team members’ first interaction with Google Cloud computing services, and it was quite a bit of a learning curve to understand what to do and why we were doing it. A great deal of struggle was had in crossing that first boundary; however, the API proved exceptionally useful, and we managed to pull through.


We came up with a novel, plausible solution to a problem that plagued our social media and morning news. To do so, we implemented Google Cloud Vision AI for real time wildfire detection, and implemented real-time geolocation tracking from an instance’s IP address.

What we learned

Most members of our team had our first experience in AI/ML working on this project. We all learned about the Google Cloud platform, and about utilizing Google’s Vision API. In addition, many of us had never worked on such a collaborative Python programming project as this, so we built up our collaboration and communication skills. We also learned how to implement multiple APIs in conjunction on a large scale project.

What's next for Wildfire Watch

  • Seamless integration with live video feed.
  • Cross correlation of fire location across multiple drones
  • Ability to directly notify and partner with local law enforcement and fire departments.
  • Pathfinding AI for drones, synchronous movement between drones to enable wide and continuous coverage.
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