The increasing in Earth Observation data availability (Landsat & Sentinel systems) and cross-cutting themes reference data (EuroCROPS, WorldCereal, Glance, GBIF), combined with the development of new machine learning approaches (semantic segmentation, auto-encoder, gradient descent tree, autoML) opened several possibilities for producing time-series spatial predictions.
However several challenges are involved in deploying ML models in a production environment considering large amount of data, including: (1) data reading/writing optimization, (2) feature selection, (3) hyper-parameter optimization, (4) time-series reconstruction, and (6) efficient parallelization.
This talk Dr. Leandro Leal Parente will share lessons learned and the computational infrastructure implemented by OpenGeoHub for producing global time-series predictions (grassland, soil carbon, FAPAR, and GPP products) within the context of Open-Earth-Monitor cyberinfrastructure (OEMC) and Global Pasture Watch projects.
11 December 2024, 14:00 - 15:00 CET
ITC Building LA 2301 / Online
For more information and registration please visit the event page.
