14  APEx Value Proposition to the SEF

The Stakeholder Engagement Facility (SEF) is an ESA-funded initiative which aims to expand stakeholder engagement for projects funded through the Applications Element of the Science for Society programme. The SEF is a crucial component of the APEx project, aimed at enhancing the interaction with various stakeholders and end-users of Earth Observation (EO) data.

14.1 SEF Objectives

  • To implement a systematic knowledge-sharing process and service.
  • To sustain and expand the interaction with stakeholders and end-users beyond the termination of individual projects.
  • To ensure the continued promotion and evolution of the deliverables from ESA Applications projects.

14.2 SEF Services and Functions

  • Showcase events and hands-on utilisation guidance.
  • Integration of tools/methods directly at the end-user processes/systems.
  • Practical utilisation exercises and demonstrations of cost-effective results.
  • Promotion of new methods and tools through practical workshops.

14.3 Thematic Focus

  • Sustainable Development Goals (SDGs)
  • Food Systems
  • Ecosystem & Biodiversity
  • Carbon, Energy & the Green Transition

These SEF instances will be the main consumers of APEx instantiation services and will serve as the primary environment for sharing EO knowledge and connecting with stakeholders.

14.4 Integration with APEx

  • SEF will leverage APEx services to create and maintain customised, configurable working environments for knowledge sharing and stakeholder engagement.
  • APEx will support SEF by ensuring that application algorithms remain available as on-demand services and by providing the necessary tools and services to facilitate knowledge sharing and management.

14.5 Operational Aspects

  • The SEF environments will aggregate project results, datasets, reproducible workflows, and development environments to support commonly encountered use scenarios by stakeholders/end-users.
  • Functionalities include demonstrations, hands-on guidance, tool/workflow integration, practical utilization exercises, and workshops.

14.6 Service Provision and Customization

  • APEx will provide services that aggregate project results, support data dissemination, and allow customization in terms of logos, themes, and backgrounds as required by the project PI.
  • Hosted application algorithms will be accessible for on-demand execution via API or GUI and linked to visualisation environments for dynamic display of results.

14.7 User Categories

APEx offers a comprehensive suite of services that provide significant value to SEF by enhancing stakeholder engagement, improving decision support, ensuring operational efficiency, and promoting sustainable impact. As such, the value proposition of APEx to the SEF can be articulated based on the three defined user categories: Data Visualization / Exploration Users, Data Analytics Users, and Data Processing Users. Each category benefits from APEx’s tailored services, ensuring that SEF can effectively engage a broad spectrum of stakeholders.

14.7.1 Data Visualization / Exploration Users

Primary Service: Dashboard Service

Use Case Scenarios

The different use case scenarios are described in the dashboard page.

Key Benefits:

  • Ease of Use: Provides intuitive, user-friendly interfaces that make it easy for non-expert users to access and explore EO data.
  • Interactive Visualizations: Offers dynamic, real-time visualizations that help users understand complex datasets through basic interactions such as zooming, panning, and toggling layers.
  • Predefined Dashboards: Supplies predefined, thematically relevant dashboards that cater to specific stakeholder interests, reducing the need for custom development and allowing immediate insight.
  • Increased Engagement: Engages a wider audience, including policymakers, educators, and the general public, by making EO data accessible and understandable.

Example

  • A time series of particule X concentration over space in several files in netCDF format
  • Dashboard with temporal scroll and display concentration level over a globe
  • Point and click tools (e.g. point, boxes, polygons) to create temporal graphs in given areas
  • Comparasion features temporal or across different dataset (or variables) including statistical tools
  • Export to PDF or movie formats
  • Update mechanism
  • Two types of interactive elements in a dashboards: if it is vector-driven or raster-driven

Example

  • Multiple sets of vector polygons and points provided as shapefiles
  • Dashboard with map and data constrained to a particular area of interest (e.g a country)
  • Sidebar with groupings and toggles for each set of polygons and points
  • Polygons and points shown as clusters on the map at higher zoom levels
  • Interaction with clusters to scale the map and show individual points/polygons
  • Polygons include references to time series raster data
  • Selection of a polygon should show time line selector for the raster data
  • Selection of both polygons and points should show graph of the time series data in a graph
  • Export graphed data as CSV

Process (internal)

  • Ingestion & Pre-processing : needs user workspace, algorithm hosting
  • Development & Setup : use something like CSV files, COG, JPEG2000, or DB
  • Operations : static view or constantly updated (redo previous steps)

14.7.2 Data Analytics Users

Primary Services: Interactive Development Environments (IDE), User Workspace Services

Key Benefits:

  • Advanced Analytical Tools: Provides tools for data manipulation, statistical analysis, and visualization customization, enabling users to extract meaningful insights from existing datasets.
  • Collaborative Workspaces: Facilitates collaboration among researchers, analysts, and other stakeholders, enhancing collective problem-solving and innovation.
  • Version Control and Data Management: Supports version control and data management, ensuring that analytical workflows are reproducible and well-documented.
  • Enhanced Decision-Making: Empowers users to perform detailed analyses that inform decision-making processes, policy development, and research initiatives.

14.7.3 Data Processing Users

Primary Service: Algorithm Hosting Service

Key Benefits:

  • High-Performance Processing: Leverages cloud infrastructure to provide scalable, high-performance computing resources for generating new datasets.
  • On-Demand Execution: Allows users to execute complex algorithms on-demand, enabling the generation of new data products and insights.
  • Reproducibility and Consistency: Uses containerization to ensure that processing environments are consistent and reproducible, reducing the risk of errors and inconsistencies.
  • Integration and Scalability: Seamlessly integrates with existing platforms and services, providing a scalable solution that grows with the needs of the users.