Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN)

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 21; p. 5099
• Main Authors: Kanwal, Rida, Rafaqat, Warda, Iqbal, Mansoor, Weiguo, Song
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Accuracy; Algorithms; analytical methods; Artificial intelligence; Artificial neural networks; Artificial satellites in remote sensing; Climate change; Climate studies; Clustering; CNN; Comparative analysis; Data analysis; data collection; Datasets; Distribution; Environmental conditions; Environmentalists; Feature extraction; fire occurrence; Forest & brush fires; fuels; Identification and classification; International cooperation; landscapes; Machine learning; Model accuracy; Neural networks; Pakistan; Physics; Precipitation; prediction; Predictions; probability; random forest; Remote sensing; Socioeconomic factors; soil; vegetation; Weather; wildfire assessment; Wildfires; Pakistan

As wildfires become increasingly perilous amidst Pakistan’s expanding population and evolving environmental conditions, their global significance necessitates urgent attention and concerted efforts toward proactive measures and international cooperation. This research strives to comprehensively enha...