Dual-Dataset Deep Learning for Improved Forest Fire Detection: A Novel Hierarchical Domain-Adaptive Learning Approach

Ismail El-Madafri (), Marta Peña and Noelia Olmedo-Torre
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Ismail El-Madafri: Department of Graphic and Design Engineering, Universitat Politècnica de Catalunya, C. Eduard Maristany 16, 08019 Barcelona, Spain
Marta Peña: Department of Mathematics, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain
Noelia Olmedo-Torre: Department of Graphic and Design Engineering, Universitat Politècnica de Catalunya, C. Eduard Maristany 16, 08019 Barcelona, Spain

Mathematics, 2024, vol. 12, issue 4, 1-27

Abstract: This study introduces a novel hierarchical domain-adaptive learning framework designed to enhance wildfire detection capabilities, addressing the limitations inherent in traditional convolutional neural networks across varied forest environments. The framework innovatively employs a dual-dataset approach, integrating both non-forest and forest-specific datasets to train a model adept at handling diverse wildfire scenarios. The methodology leverages a novel framework that combines shared layers for broad feature extraction with specialized layers for forest-specific details, demonstrating versatility across base models. Initially demonstrated with EfficientNetB0, this adaptable approach could be applicable with various advanced architectures, enhancing wildfire detection. The research’s comparative analysis, benchmarking against conventional methodologies, showcases the proposed approach’s enhanced performance. It particularly excels in accuracy, precision, F1-score, specificity, MCC, and AUC-ROC. This research significantly reduces false positives in wildfire detection through a novel blend of multi-task learning, dual-dataset training, and hierarchical domain adaptation. Our approach advances deep learning in data-limited, complex environments, offering a critical tool for ecological conservation and community protection against wildfires.

Keywords: forest fire detection; remote-sensing forest monitoring; deep learning; domain adaptation; hierarchical learning; convolutional neural networks; transfer learning; dual-dataset training; multi-task learning; false positives (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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