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Wildfire Dataset Helps Firefighters Save Lives, Property

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An airplane drops fire-extinguishing chemicals on a wildfire.

The WildfireDB dataset has mapped historical fire data in the contiguous U.S. between 2012 and 2017.

Credit: Mike Newbry

Scientists at the University of California, Riverside (UCR), Stanford University, and Vanderbilt University have built the WildfireDB dataset for simulating the spread of wildfires to help study wildfires and guide emergency response and evacuations.

UCR's Ahmed Eldawy called WildfireDB "the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography."

The dataset is a compilation of information on the spread of fires in the contiguous U.S. over the last decade.

The researchers employed the Raptor satellite data processing system to combine data on historical wildfires with other geospatial features; researchers or firefighters can select relevant data from the dataset to train machine learning models to forecast wildfire dynamics.

From UC Riverside News
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