Review of Real-Time Monitoring and Early Warning Technology for Natural Disasters Based on Multimodal Information Fusion

Multimodal information fusion technology shows great potential in real-time intelligent monitoring and early warning of natural disasters. By integrating remote sensing images, ground sensor data, social media text, UAV video and other heterogeneous data sources, the technology significantly improves the accuracy of disaster monitoring and timeliness of early warning. It shows that the global economic losses caused by natural disasters from 2015 to 2024 exceeded US $3 trillion, affecting billions of people, highlighting the social and scientific significance of an efficient early warning system. This paper mainly summarizes the application progress of multimodal information fusion in earthquake, flood, and typhoon monitoring in the past decade. However, the field still faces challenges, including data heterogeneity, computational complexity, insufficient model generalization ability and cross regional collaboration difficulties. For these reasons, researchers have proposed a number of solutions, such as cross modal alignment technology, generation of confrontation network to fill the missing data, transfer learning to improve the generalization ability of the model, federal learning to ensure data privacy. Future research directions will focus on adaptive fusion algorithm, application of generative AI technology, construction of global collaboration network and promotion of low-cost technology. Facing the challenges in the future, we need interdisciplinary cooperation and technological innovation to jointly overcome the existing problems, reduce the losses caused by natural disasters, and protect the safety of human life and property.