Advancements in remote sensing for active fire detection: A review of datasets and methods.

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Abstract

This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical models with machine learning, and building digital twins for data analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of RS technology.Copyright © 2024. Published by Elsevier B.V.