A Review of Typhoon Detection, Tracking and Intensity Estimation Using Deep Learning and Multi-modal Remote Sensing Data

Typhoons rank among the most destructive natural disasters globally, inflicting substantial casualties and enormous economic losses across the world each year. Accurate typhoon detection, tracking, and intensity estimation are crucial for disaster warning and risk management. Traditional typhoon monitoring methods primarily rely on numerical weather prediction models and expert judgment, which suffer from limited accuracy and insufficient timeliness. In recent years, the rapid development of deep learning technologies has brought new opportunities to typhoon research, particularly demonstrating significant advantages in multi-modal remote sensing data fusion and automated feature extraction. This paper systematically reviews the current state of typhoon detection, tracking, and intensity estimation technologies based on deep learning, analyzes the application of multi-modal remote sensing data in typhoon monitoring, discusses current technical challenges, and prospects future development trends. Research indicates that deep learning methods show superior performance in automated typhoon feature recognition, temporal sequence modeling, and multi-source data fusion, providing new technical pathways for improving typhoon forecasting accuracy and operational efficiency.