Summer School: AI for Earth Observation and Scalable Data Management
Held in Ljubljana from 22nd to 25th of September 2025, this intensive summer school offers a unique opportunity to delve into the cutting-edge intersection of Artificial Intelligence (AI) and Earth Observation (EO), combined with the essential skills for managing large-scale data and workflows in modern computing environments. Participants will gain theoretical knowledge and practical experience in applying AI techniques to analyze EO data, optimizing AI performance, managing complex workflows with Kubernetes, and handling massive datasets.
High Performance Kubernetes (HPK)
Bridging Cloud-Native Workflows and HPC for DaFab
One of the main goals of the DaFab project is to enable seamless, multisite scientific workflows—without the need to physically move data between sites. This is crucial in environments where data transfer is restricted by bandwidth, administrative domains, or security policies. One of the main DaFab’s goals is to orchestrate distributed workflows across multiple HPC and cloud sites, letting data stay in place while computation moves to where it’s needed.
State-of-the-art review for crop field boundaries delineation from satellite data
Introduction
The delineation of agricultural field boundaries from satellite imagery is crucial for precision agriculture, land management, policymaking and crop monitoring.
It can be a stand-alone solution or as the first stage for crop classification, advanced soil moisture analysis, yield forecast, the estimation of damages to crop yield due to natural (e.g. drought, floods) and more.
In the DaFab project, field delineation, which involves outlining agricultural parcels with polygons, offers a solution to several difficulties encountered in yield forecasting and crop classification tasks. This approach is more effective than traditional pixel-by-pixel classification maps because of the reasons: