Advancing Data Fusion in Earth Sciences

Artificial intelligence (AI) algorithms have proven to be quite effective in Earth observation applications, often, when extensive amounts of representative training data are available. At large, processing large volumes of observation data can be challenging due to a myriad of reasons that include the cost of acquiring labeled samples, computing resources, identifying critical data features for model prototyping, standardization of model building, and deployment. Practical novel tools and approaches are emerging across different communities. In this paper, we discuss several such recent methods from machine learning and share lessons from advanced, scalable workflows that could impact the advancement of multimodal data fusion for Earth Science applications.