Tag: Continuous Latent Space

Abstract: Recent advances in remote sensing have expanded Earth observation through an increasing number of sensing modalities, where each sensor provides distinct spectral, spatial, and temporal information. Effective analysis of these data depends on learning representations that integrate complementary information across modalities while supporting multiple learning objectives. Multi-task multi-modal learning in remote sensing remains challenging […]

Congratulations to Dr. Meilun Zhou for successfully passing his PhD dissertation exam! As a member of the Machine Learning and Sensing Lab, his research focused on developing advanced methods for multi-task, multi-modal representation learning in remote sensing. His work introduces an annotation-driven framework that creates more informative and aligned representations across diverse sensing modalities, helping […]