Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
This work addresses wildfire prediction for land and forestry management, but it is incremental as it applies existing deep learning methods to a new dataset.
The researchers tackled the problem of predicting wildfire-prone regions by aggregating nearly a decade of remote-sensing and historical fire data, achieving an AUC of 83% using deep learning models.
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.