Assessment of LiDAR-Based Atmospheric Observations Using YOLOv9 for Sky Image Recognition

This study introduces an innovative method to improve the quality and accuracy of LiDAR observation data using YOLOv9 image recognition technology. By correlating daytime cloud image grayscale values and nighttime sky star counts with LiDAR data, this method provides a robust solution for all-weather observations. Experimental results show that this technique can accurately predict LiDAR data quality under various environmental conditions, offering a valuable tool for atmospheric science research and meteorological monitoring.