Water change detection is one of the most critical applications of modern remote sensing. As climate volatility increases, the ability to accurately quantify surface water extent and soil moisture levels has become a priority for hydrologists, agricultural practitioners, policymakers and urban planners alike. Utilizing multi-spectral (Sentinel-2) and Synthetic Aperture Radar (Sentinel-1) data, we can now map hydrologic anomalies (droughts or floods) with unprecedented precision.

In the DaFab project, the water change detection workflow is used to enrich satellite data with additional parameters that indicate flood or drought in the scenes. It helps in optimizing the search, automating the selection of the past flood or drought events visible in Sentinel-1 / Sentinel-2 images and finally monitoring, analysing the affected areas from the generated water masks.

The role of SKIM in DaFab system architecture

Introduction: Turning a Copernicus data scale archive into something you can search

Copernicus has grown into an archive where the limiting factor is rarely the availability of pixels, but the ability to find the right ones. To do so, users still have to start from copernicus product descriptors (e.g. Sentinel-2 tile, date, processing level, etc…) and only later test whether the data contains the signal of interest. DaFab system addresses this gap by generating secondary, AI‑derived metadata at scale and exposing it as a discovery surface, so that users can begin with a thematic question instead of beginning with file selection (e.g. “How many agriculture parcels are there ?” in smart-agriculture thematic and “Where can I find water anomalies ?” for water-analysis one).

In our previous DaFab post, we introduced the overall multi-site orchestration vision. This entry focuses on a specific architectural building block: integrating Karmada with Argo Workflows to enable multi-cluster and multi-site workflow execution driven by rule-based placement. The key outcome is that workflow steps can be dispatched to different Kubernetes clusters (sites) based on explicit rules that reflect data locality and computing resource availability goals, without changing how users author workflows.

Navigating Strategic Pivots and Technical Milestones

DaFab has now reached half of its length, an ideal time to look back in the rear-view mirror and to reassess our roadmap. After 18 month of work we can claim substantial advancements across DaFab work packages, demonstrating agility and a solid technical foundation, despite facing initial challenges such as delayed deliverables and staffing issues. The project has successfully established a robust management structure, implemented a significant technological pivot, and achieved initial technical milestones, laying the groundwork for its full deployment.

Usability, Impact, and a New Horizon for DaFab and the Global Rucio Community

Over the past year, the DaFab project has become a catalyst for the evolution of the Rucio data management system. While initially designed to support the ATLAS experiment at Cern, today Rucio serves a far wider community of scientific collaborations with complex data needs. The DaFab initiative, centered on extracting value from massive Copernicus Earth Observation archives, has pushed Rucio into new territory, beyond file cataloguing and distributed data placement, and into the realm of rich semantic metadata and powerful filtering.

Workflows processing Earth Observation (EO) data have a problem – the body of available EO data is vast. And growing rapidly. Within the DaFab EU project, AI-driven workflows must process massive quantities of EO data, made available by the Copernicus project, in an efficient and reliable manner. This presents a range of problems, including locating the relevant data, decoupling relatively fast and scalable compute tasks from slower data transfers, storing the data in a way that the workflows can use it, and managing the lifetime of any temporary copies required. This is where DASI (the Data Access and Storage Interface) plays a critical role. It provides the smart bridge between storage systems and compute environments. DASI’s semantically driven data management design helps build intelligent, scalable, and optimized AI workflows in the DaFab project.

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.

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.

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:

DaFab workshop will be held at High Performance, Edge And Cloud computing conference HiPEAC 2025 in Barcelona. Project members will showcase project plans and technologies and present an on-line demonstration of existing prototype. The workshop will be held on January 20th in Barcelona.