Artificial intelligence and remote sensing frameworks for wildfire monitoring and risk analysis across multiple ecosystems: a review

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Abstract

In this review, we synthesized advancements in remote sensing and machine learning (ML) for wildfire risk assessment, early detection, and spread prediction across forests, grasslands, shrublands, and WUI ecosystems. We examined the capabilities and limitations of key satellite, UAV, and multisensor datasets, alongside the performance and transferability of ML and deep learning models. Major research gaps were identified in cross-ecosystem generalization, real-time data integration, model optimization, and uncertainty quantification. In the review, we contribute an ecosystem-inclusive comparison of RS–AI frameworks and outline future directions for operational, next-generation wildfire monitoring.

Keywords

artificial intelligence / climate change / fire risk assessments / machine learning / NBR / remote sensing & GIS / wildfire prediction / real time fire detection

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