Forest Fires and Artificial Intelligence: A Contemporary Literature Review
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Abstract
Forest fires have become increasingly frequent and severe due to climate change and human activities, creating urgent demands for effective monitoring and predictive systems. This study provides a comprehensive literature review examining recent advances in artificial intelligence (AI) applied to forest fire detection, prediction, and post-fire assessment. Findings show that the integration of remote sensing data—particularly from Sentinel-2, Landsat, MODIS, and VIIRS—with machine learning and deep learning models significantly improves the accuracy of fire detection and burn severity mapping. AI-based prediction models, including Random Forest, Support Vector Machines, LSTM networks, and hybrid architectures, demonstrate strong capabilities in forecasting fire occurrence when combined with meteorological and vegetation indices. Moreover, the adoption of cloud-based platforms such as Google Earth Engine has accelerated real-time processing and operational scalability. Despite these advancements, challenges persist related to data scarcity, model generalizability, cloud obstruction in optical imagery, and inconsistent validation methods. Overall, this review confirms the growing importance of AI in modern wildfire management and highlights critical directions for future research, including multimodal data fusion, transfer learning, real-time sensor integration, and interdisciplinary collaboration.
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