Predicting wildfire burns from big geodata using deep learning

• Published in: Safety science Vol. 140; p. 105276
• Main Authors: Bergado, John Ray, Persello, Claudio, Reinke, Karin, Stein, Alfred
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.08.2021; Elsevier BV
• Subjects: Big geodata; Burns; Convolutional networks; Crop fields; Datasets; Deep learning; Feature extraction; Forest & brush fires; Ground-based observation; Input statistical importance; Land cover; Land surface temperature; Land use; Lightning flashes; Multilayer perceptrons; Power lines; Regression coefficients; Remote sensing; Remote sensors; Risk management; Spatial data; Statistical analysis; Wildfire burn prediction; Wildfires; Wind direction; Woodlands; Deep learning; Big geodata; Convolutional networks; Wildfire burn prediction; Input statistical importance

•Convolutional network for predicting daily maps of the probability of a wildfire burn.•Convolutional networks demonstrate higher predictive accuracy and map quality.•Exploratory feature statistical importance metrics improves model transparency. Wildfire continues to be a major environmental proble...