With the continuous improvement in computer science and HPC systems on one hand, and the availability of more accurate and frequent observations on the other hand, especially satellite-based ones, data-driven models, namely AI-based and machine learning models, are becoming more efficient and accurate in simulating the Earth system. These developments opened a paradigm shift between considering physical-based models and data-driven ones, or even both combined in the so-called hybrid systems.
Machine Learning for Land Modeling (ML4LM) aims at exploring the extent and the role that machine learning would play for better land surface studies, especially identifying the main areas where it could be applied and providing tools and data to the land surface modeling community. It is a project of the GLASS Panel, which coordinates the evaluation and intercomparison of the latest generation of land models and their applications to scientific queries of broad interest.
2025 ML4LM Webinar Series
The ML4LM webinar series gathers eminent scientists to share their experience in these combined fields. Visit the webinar page for the 2025 schedule.
The link for January’s webinar is live! Register for the webinar by clicking here. After you register, the link for the webinar will be emailed to you.
