Currently, NASA satellite imagery of detected forest fires is not used in real-time for fire prevention. One potential reason is that the detected fires from this data set contain a high number of false positives, ranging from hot asphalt parking lots to house fires to farmer burn piles.
In his data science capstone project, graduate Sean Sall built a model that could identify which of these detected fires are actually forest fires using historical fire perimeter boundaries and weather data. The end goal is to accurately identify fires so that the detected fires data set can be used in near real-time to aid forest fire prevention, where every minute counts.
Sean explains how he detected forest fires:
Interested in learning more about his project? Take a look at the github repo.
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