Ground Based

Ground-based research encompasses a wide range of scientific endeavors leveraging data collected from Earth's surface, aiming to improve understanding and prediction across diverse fields. Current research focuses heavily on developing and applying advanced machine learning models, including deep convolutional autoencoders, Swin Transformer UNets, and transformer networks, to analyze this data for applications such as atmospheric correction in astronomy, precision agriculture, and weather forecasting. These efforts are significantly improving the accuracy and efficiency of data analysis, leading to enhanced capabilities in areas like environmental monitoring, resource management, and astronomical observation. The resulting improvements in data processing and analysis have broad implications for various scientific disciplines and practical applications.