ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-4/W1-2022
https://doi.org/10.5194/isprs-annals-X-4-W1-2022-483-2023
https://doi.org/10.5194/isprs-annals-X-4-W1-2022-483-2023
14 Jan 2023
 | 14 Jan 2023

THE LARGE-SCALE WILDFIRE SPREAD PREDICTION USING A MULTI-KERNEL CONVOLUTIONAL NEURAL NETWORK

M. Marjani and M. S. Mesgari

Keywords: Wildfire, Multi-kernel CNN, Deep learning, Convolutional neural network, Forest fire, Machine learning

Abstract. In the last twenty years, destructive wildfires have affected the environment to the tune of billions of dollars. An accurate model is crucial for predicting the spreading of wildfires in a variety of conditions. In this study, a multi-kernel convolution neural network (CNN) deep learning model was proposed based on elevation, wind direction, and speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), and energy release component to predict wildfire spread across the United States. Using multi-kernel CNN, it is possible to predict whether a pixel will be on fire at a future time. Compared to the model presented by other authors, the multi-kernel CNN model achieved high accuracy and F1 score. In comparison with CNNs without a multi-kernel mechanism and fixed kernel size, the proposed model predicted more accurate results based on the test data set. The multi-kernel CNN model reached an overall accuracy of 98.6 and F1 score of 70.97 on test data.