Remote sensing technology has facilitated data acquisition over large and small spatio-temporal scales resulting in valuable large datasets. Present advances in technology with availability of large computing and scalable storage allow us to explore these datasets in a unique way to use data-driven techniques to learn patterns, understand physical processes, and characterize feedbacks in the Earth system. Among the many fields within data exploration, Machine Learning (ML) has had a major impact on not only scientific thinking but also in commercial applications. ML's advantages include, application flexibility and scaling, fast running time, and ability to represent complex relationships from historical data in conjunction with physics-guided processes. This session is a platform to discuss advancements in the applications of ML in Earth science and remote sensing as well as challenges and future opportunities for ML applications. Presentations will address the merger of discrete mathematics, statistics, physics, and non-linear optimization techniques.
Conveners:Hamed Alemohammad, Pierre Gentine, Joel Gongora, and Sangram Ganguly