Open Earth Monitor — Global Workshop 2023

Iacopo Ferrario

Geospatial Analyst


EO-based Use Cases for EU Policy Support
Ilaria Gliottone, Candan Eylül Kilsedar, Iacopo Ferrario

The European Commission Knowledge Centre on Earth Observation (KCEO) is developing two use cases, aiming to provide sustained last-mile products and services emerging from the analysis of EU policy needs, EU Framework Programmes for Research and Innovation and GEO activities.

The first use case to transfer into a sustained product and service has emerged from the evaluation of research projects in connection to EuroGEO. In this respect, the EuroGEO Showcases: Applications Powered by Europe (e-shape) project, having received funding from the H2020 programme and aiming to ensure the optimal implementation of EuroGEO is the primary project to evaluate. The pilots of the e-shape project were the candidate use cases considered for implementation and further development by KCEO. They have been evaluated by criteria that consider policy relevance and technical aspects, such as data sources and infrastructures and European principles related to these. As a result of the evaluation, KCEO selected the photovoltaic energy assessment at an urban scale pilot led by ARMINES as the first adopted project. Through the implementation of use cases in a prototypical EuroGEOSS virtual ecosystem, it will also be possible to define the good practices and technologies to be used in the future, operational EuroGEOSS more thoroughly. The use case will be shaped according to the needs of the policy Directorate-Generals (DGs) through the KCEO Deep Dive on Climate Change Adaptation in Urban Areas in collaboration with ARMINES.

The second use case focuses on monitoring wetlands’ change and degradation processes across EU Natura 2000 (N2K) sites. The use case has been developed from the identification of DG Environment (ENV) policy needs at the policy implementation and evaluation stages of the Habitats Directive and implements the results of the chapter on wetlands of the KCEO Deep Dive on Biodiversity assessment (upcoming science for policy report in 2023). The goal of the use case is to cover the last mile of the EO value chain to enable the full exploitation of EO products and derived products and to foster their uptake in policy making. This should result from 1) requirements translation and co-development with DG ENV stakeholders, 2) fitness for purpose analysis of existing products and applications, 3) co-design of the web application information content, graphics and features, 4) identification of gaps and recommendations for the improvement of products and 5) provision of a working prototype as a proof of concept for an operational service. The project will be framed around three spatial scales of interest: pan-European, river basin and N2K site and four application services: habitat mapping, pressure and condition trend analysis, pressure and condition monitoring and hotspot analysis. The use case acknowledges the importance of integrating EO data from multiple sensors with other data, including hydrological modelling outputs.

The adoption and development of these use cases will answer the specific needs of the EU policies, increase the use of Copernicus data and services and provide visibility to the projects identified generating more value from the investments already made.

Poster presentation
Advancing Earth Observation knowledge management through machine learning and semantic interoperability for EU policy support
Dominik Weckmüller, Iacopo Ferrario

The Knowledge Centre on Earth Observation activity is grounded in sound knowledge management practices and cutting-edge NLP technologies. It aims to create a common scaffolding for research projects on the one hand and policy needs on the other.

The User Requirement Database (URDB) stores and validates Core Copernicus Users requirements for Earth Observation (EO) products and applications. The URDB facilitates automated gap analysis and screenings across diverse data spaces in pursuit of the optimal matching pre-existing solution, initially examining Copernicus Services product catalogues, followed by a subsequent exploration of research findings from the EU Horizon programme. The Text Mining Application (TMA) leverages innovative advancements in machine learning utilizing Transformers to facilitate precise semantic document retrieval within an EO-specific subset of research outcomes financially supported by the European Union's research and innovation framework programmes. These programmes and the respective EO project data span from FP1 in 1984 to the most current initiative, Horizon Europe. The primary TMA objective is to empower users with rapid access to research findings for highly specific queries, while simultaneously offering a user-friendly database, an internal microservice as an API, and a GUI interface for more advanced metrics and visualizations. In the future, the URDB and TMA will be closely interlinked and integrated, enabling users of either platform to benefit from rapid access to the actual Copernicus datasets, as well as enhanced meta-information metrics and insight into research outcomes.

In addition to supporting gap and fit for purpose analysis, the main scope of the URDB is to enable requirement retracing across the components of the EO value chain, from policy needs to observations, and therefore supporting and tracking the evolution of the Copernicus Programme. The URDB's records are technology-agnostic quantitative requirements, expressed by verifiable, unambiguous and actionable technical specifications (horizontal resolution, measurement uncertainty, tasking time, etc.). The URDB's data model builds on the experience of existing requirement databases from Copernicus Core Services and international partners (e.g., USGS and NASA). One of the URDB’s and TMA’s core design principles is semantic interoperability: entities, relationships and attributes are clearly defined in a terminology and, when applicable, they follow international standards (ISO, OGC), recommendation and best practices (CEOS, GEO).
From a technical perspective, both, the URDB and TMA are self-hosted open-source databases with a GUI and an application layer for querying and performing analysis. While the URDB is based on PostgreSQL, the TMA utilizes a novel vector database known as Qdrant, which fulfils highly specific AI requirements and offers a user-friendly API.
The vision for both databases is to link them through web APIs to online data catalogues and tightly integrate them into the existing (meta-) data Copernicus landscape.

Poster presentation