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:

Introduction

In the realm of both scientific research and production environments, efficiently managing and utilizing metadata is crucial. Metadata serves as the backbone for data discovery, organization, and retrieval, enabling effective data usage across various fields. This is particularly important in areas like Earth Observation (EO), where vast amounts of satellite data need to be processed and analysed to monitor and understand our planet.

The DaFab project, an ambitious initiative, aims to enhance the exploitation of Copernicus data through advanced AI and High-Performance Computing (HPC) technologies. By integrating these technologies, DaFab seeks to improve the timeliness, accuracy, and accessibility of EO data. At the heart of this endeavour lies Rucio, a robust data management system developed by CERN. Rucio’s role is pivotal in achieving key objectives of the project such as creating a unified, searchable catalogue of interlinked EO metadata, improving metadata ingestion and retrieval speeds, and facilitating seamless integration with AI-driven workflows and HPC systems.

Introduction

In the realm of both scientific research and production environments, efficiently managing and utilizing metadata is crucial. Metadata serves as the backbone for data discovery, organization, and retrieval, enabling effective data usage across various fields. This is particularly important in areas like Earth Observation (EO), where vast amounts of satellite data need to be processed and analysed to monitor and understand our planet.

The DaFab project, an ambitious initiative, aims to enhance the exploitation of Copernicus data through advanced AI and High-Performance Computing (HPC) technologies. By integrating these technologies, DaFab seeks to improve the timeliness, accuracy, and accessibility of EO data. At the heart of this endeavour lies Rucio, a robust data management system developed by CERN. Rucio’s role is pivotal in achieving key objectives of the project such as creating a unified, searchable catalogue of interlinked EO metadata, improving metadata ingestion and retrieval speeds, and facilitating seamless integration with AI-driven workflows and HPC systems.

Multi-Site Workflow Orchestration in the DAFAB Project

DAFAB will design and implement a workflow orchestration system that enhances multi-site application deployment and data discovery. The workflow system will enable applications to express their computations as a graph and declare the data needed at each step in a high-level query language. Workflows will then execute across multiple sites, whether cloud-based (Kubernetes) or high-performance computing (Slurm) environments. By enabling transparent data access and seamless execution of workflow stages, this system shifts the burden of application deployment and data discovery to the platform itself, significantly accelerating development timelines.

Data economy is an ever-increasing market. Thanks to the Copernicus constellation Europe has amassed a considerable amount of Earth Observation data already used by technology-intensive SMEs and large industry in several sectors of the economy. However, the management of data at scale remains challenging, and lowering the entry cost to exploitation of Copernicus data is a key element to foster innovation and fuel this data economy. #DaFab is a new project, launched with the support of the European Space Agency: https://www.euspa.europa.eu/dafab-ai-factory-copernicus-data-scale