Global Scale

Global-scale research focuses on analyzing and modeling large-scale datasets and complex systems across the entire planet, aiming to understand global patterns and processes. Current research utilizes various machine learning approaches, including graph neural networks, deep learning surrogate models, and transformers, to address challenges in areas such as wildfire prediction, biodiversity monitoring, and crop mapping. These advancements enable more accurate and efficient analyses, leading to improved insights into global phenomena and informing crucial decision-making in environmental management, resource allocation, and other critical domains.