In the context of the AGEMERA project (European Union's Horizon Europe research and innovation programme under grant agreement No 101058178), OPT/NET introduced the AGEMERA Platform as an extension to its flagship product, MONITORED AITM. AGEMERA's core objectives include unlocking the EU's resource potential, enhancing public awareness of critical raw materials, and fostering environmentally and socially responsible mineral exploration.

OPT/NET solutions deliver exceptional user experience through an intuitive AI-driven interface, streamlining the complex processes of remote sensing data acquisition and data fusion to boost productivity by merging AI processing and automation with human problem-solving skills. The AI engine optimally harnesses heterogeneous Earth Observation data (predominantly  Copernicus Programme) and analysis-ready data from multiple heterogeneous and multimodal sources, promoting responsible mineral exploration and addressing environmental concerns, resulting in an interactive user-friendly web platform.

The platform is structured around three main modules:

• OCLI 

Serving as the backend, OCLI manages data mining processes and automates functions, such as data acquisition, preprocessing, image processing, thorough workflows known as AI Knowledge Packs (AIKPs). OCLI collects relevant satellite data products, prepares analysis-ready data (ARD) cubes, and acts as the foundation for multi-source data fusion.

• AGEMERA Geo-Suite Graphical User Interface 

As the frontend module, this web-based platform offers a general data repository and visualization in Cloud Optimized data formats. Powered by OGC standards compliant web services, it enables interactive exploration of extensive datasets for the public and mine site operators and owners. The conversational AI agent assists in easy navigation through the extensive collection of the data and insights in the platform, while the data itself is secured with various authentication and authorization access levels.

• Data Repository

Comprised of bulk data warehouse enriched with INSPIRE nomenclature compliant metadata, the Data Repository employs Cloud Object Storage Service as the primary storage solution. This service provides web-based access to the object storage, ensuring efficient data access from any web browser.

OPT/NET's AI platform, exemplified in the AGEMERA project, stands as a pioneering solution seamlessly integrating AI processing, heterogeneous data fusion, human problem-solving, and advanced data management for generation of impactful insights and responsible resource exploration.

How to cite: Stimac Tumara, B. and Matselyukh, T.: AGEMERA AI: Innovative AI solution for responsible resource exploration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-628, https://doi.org/10.5194/egusphere-egu24-628, 2024.

The Copernicus Data Space Ecosystem introduces an innovative approach to accessing large Earth Observation (EO) data through streaming services and APIs. This study elaborates on advantages of the novel data discovery, streaming and access APIs within the Data Space, emphasizing key components such as: STAC catalog, OData and OpenSearch querying APIs, Browser, S3 interface, Jupyter Hub, openEO, and Sentinel Hub’s APIs.

The OData (Open Data Protocol) stands as the ISO/IEC approved standard, outlining best practices for designing and utilizing REST APIs. It harmonizes various elements such as request and response headers, status codes, HTTP methods, URL conventions, media types, payload formats, and query options. The OpenSearch catalogue enables the search for Copernicus data through a standardized web service. For technical details, the OpenSearch specification serves as a reference. The results from this web service are delivered as GeoJSON feature collections, with each feature representing an Earth observation 'product' and referencing the location of the actual data.

STAC (Spatio-Temporal Asset Catalog) emerges as a cutting-edge EO data management standard with robust data discovery and access capabilities. Built on simple JSON entries with resource-pointing URLs, it boasts flexibility in the types of data stored and accommodates new extensions and capabilities seamlessly. The hierarchical structure includes collections (e.g., Sentinel-2 L2A), subdivided into items representing satellite products. These items further break down into assets corresponding to spectral bands. This detailed STAC catalogue facilitates user searches for specific spectral bands and enables streaming of spatial data subsets using HTTP range requests.

JupyterHub is a central platform for managing Jupyter Notebooks, simplifying tasks like resource allocation and user authentication. Copernicus Data Space Ecosystem users get free access, but with limited resources. Jupyter Notebooks offer a user-friendly interface for EO data prototyping, integrating with Python SDKs like Sentinel Hub and OpenEO. They grant direct access to the EO data repository, eliminating dependency hassles with pre-configured kernels for immediate prototyping. 

Sentinel Hub, a satellite imagery processing service, that excels in on-the-fly actions like gridding, re-projection, mosaicking, etc. It efficiently fetches data for web applications or analytical processes like ML, based on satellite data without replication or pre-processing. Several APIs, including OGC services, contribute to the Data Space Browser for advanced data visualization. Key APIs within the Copernicus Data Space Ecosystem include: BYOC (Bring Your Own COG) API, (Batch) Processing API, (Batch) Statistical API. These APIs empower users to perform diverse tasks, from data ingestion to advanced statistical analyses.

OpenEO, revolutionizes geospatial data processing and analysis by offering a unified, interoperable platform for developers, researchers, and scientists. This framework empowers users to address complex geospatial challenges collaboratively, leveraging distributed computing environments and cloud-based resources. The collaborative nature of openEO enables seamless sharing of code, workflows, and data processing methods across platforms, advancing the accessibility, scalability, and reproducibility of Earth Observation (EO) data. OpenEO's intuitive libraries in Python, R, and JavaScript simplify the analysis of diverse datasets. In addition to the SDK, openEO features the Web Editor - an online tool for visually constructing workflows.

How to cite: Musial, J., Leszczenski, J., Bojanowski, J., Milcinski, G., Vrecko, A., Clarijs, D., Dries, J., and Marquard, U.: Overview of the Copernicus Data Space Ecosystem APIs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10109, https://doi.org/10.5194/egusphere-egu24-10109, 2024.

Several scientific fields, including climate science, have undergone radical changes in the last years due to the increase in the data volumes and the emergence of data science and Machine Learning (ML) approaches. In this context, providing fast data access and analytics has become of paramount importance. The data space concept has emerged to address some of the key challenges and support scientific communities  towards a more sustainable and FAIR use of data.

The ENES Data Space (EDS) represents a domain-specific example of this concept for the climate community developed under the umbrella of the European Open Science Cloud (EOSC) initiative by the European Commission. EDS provides an open, scalable, cloud-enabled data science environment for climate data analysis on top of the EOSC Compute Platform made available through a user-friendly JupyterLab GUI. The service integrates into a single environment climate datasets from well-known initiatives (e.g., CMIP6), compute resources, data science and ML tools. It was launched in the context of the EGI-ACE project, it is accessible through the EOSC Catalogue and Marketplace (https://marketplace.eosc-portal.eu/services/enes-data-space) and it also provides a web portal (https://enesdataspace.vm.fedcloud.eu) including information, tutorials and training materials. It has recently been added to the Data Spaces radar, an initiative launched by the IDSA (International Data Space Association) with the main goal of mapping data spaces from several domains into one easy-to-use tool.

The EDS is being employed in climate applications targeting big data processing, interactive analytics and visualization, and recently it has been extended to support more advanced scientific applications based on ML. In particular, it has been enhanced to provide a cloud-based development and testing environment for the implementation of some data-driven Digital Twin applications for extreme climate events in the context of the interTwin project. Finally, the ENES Data Space will be also one of the pilots in EOSC Beyond, a Horizon Europe project where the EDS will integrate and validate the new EOSC Core capabilities developed by the project.

This work has been supported in part by interTwin; interTwin is funded by the European Union (Horizon Europe) under grant agreement No 101058386.

How to cite: Elia, D., Antonio, F., Fiore, S., Donno, E., Accarino, G., Nassisi, P., and Aloisio, G.: A Data Space environment for big data and ML-based climate applications in the European Open Science Cloud, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17472, https://doi.org/10.5194/egusphere-egu24-17472, 2024.

The Green Deal Data Space is born in the big data paradigm where sensors produce constant streams of Earth observation data (remote sensing or in-situ). The traditional and manual organization of data in layers is no longer efficient as data is constantly evolving and mixed together in new ways. There is a need for a new organization of the data that favors users and in particular can be considered ready for use. The AD4GD project proposes a threefold solution: A new information model, dynamic multidimensional datacubes and OGC APIs for data query and filtering. The use of data in the context of the Green Deal is difficult due to the wide heterogeneity of data sources (many times expressed in different data models) and different semantics used to represent data, and the lack of sufficient interoperability mechanisms that enable the connection of existing data models. The solution is a framework composed by a suite of ontologies implemented in line with best practices, reusing existing standards and well-scoped models as much as possible and establishing alignments between them to enable their interoperability and the integration of existing data. This approach was successfully applied to object based data stored in a RDF triple store e.g. vector data and sensor data) in the agricultural sector. The traditional representation of static two-dimensional layers needs to be replaced by a dynamic view where time becomes an extra dimension. In addition, other dimensions emerge such as the height in atmospheric and oceanic models, the frequency in hyperspectral remote sensing data or the species in a biodiversity distribution model. These dimensions define a data cube that offers an entry to a constantly evolving world. The dimensions in the datacube as well as the values stored in the cube cells (a.k.a. attributes) should be connected to the concepts in the Information Model. In the big data paradigm access to individual layers is difficult due to the dynamic nature of the data. Instead we need a set of modern APIs as an entry point to the data in the data space. The OGC APIs offer a set of building blocks that can be combined together with other web API design principles to build the Green Deal Data Space data access APIs. Users will be able to get the necessary data for modeling and simulating the reality using the OGC APIs endpoints described in the OpenAPI description document. The OGC APIs include the concept of collections as an initial filtering mechanism but a filtering extension using CQL2 is in advanced draft status. It is fundamental that the OGC APIs are also capable of generating detailed metadata about the extracted subset including data sources, producer information and data quality estimations.

AD4GD is a Horizon Europe project co-funded by the European Union, Switzerland and the United Kingdom.

How to cite: Masó, J., Brobia, A., Zamzov, M., Serral, I., Hodson, T., Palma, R., Bastin, L., and Lush, V.: OGC Web APIs to Make Data Available in the Green Deal Data Space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18047, https://doi.org/10.5194/egusphere-egu24-18047, 2024.

Integrity of natural ecosystems, including terrestrial ones, and their connectivity is one of the main concerns of current European and Global Green Policies, e.g., the European Green Deal. Thus, public administration managers need reliable and long-term information for a better monitoring of the ecosystems evolution and inform decision making. Data Spaces are intended to become the EC comprehensive solution to integrate data from different sources with the aim to generate and provide a more ready to use knowledge on climate change, circular economy, pollution, biodiversity, and deforestation.

The AD4GD project does research on the co-creation of the European Green Deal Data Space as an open space for FAIR data and standards-based services tested in 3 pilot cases providing testbeds in terms of data, standards, sharing and interoperability. One of these pilots, is focused on Ecological Terrestrial Connectivity in Catalonia (NE of Spain).

The challenges are: (1) monitoring ecological connectivity in terrestrial ecosystems through the integration of state-of-the-art multi-sensor remote sensing imagery, ecological models, in-situ biodiversity observations and sensors; and (2) forecasting ecological connectivity to help to define effective actions to reduce terrestrial biodiversity loss.

To this goal, solutions are being proposed and tested, to integrate data from different sources using modern standards such as, raster-based land cover maps and connectivity maps structured as data cubes (Open Data Cube and Rasdaman), GBIF species occurrences exposed via OGC STAplus, as well as data from low-cost automatic sensors (camera traps with species identification software). All this data is related together by the use of semantic tagging with references pointing to vocabularies of GEO Essential Variables stored in OGC RAINBOW Definition Server.

AD4GD is a Horizon Europe project co-funded by the European Union, Switzerland and the United Kingdom.

How to cite: Serral, I., Bastin, L., Kriukov, V., and Masó, J.: Data Spaces as an exploratory solution for big data biodiversity challenges in support of the European Green Deal: the case of terrestrial habitat connectivity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19101, https://doi.org/10.5194/egusphere-egu24-19101, 2024.

Identification of crops over large areas is necessary for monitoring agricultural production, establishing food security policies, or controlling compliance with the Common Agricultural Policy. Data acquired by Sentinel-1 and Sentinel-2 satellite constellations with a combination of high temporal and spatial resolutions has already demonstrated, separately or fused, its ability to classify crops. However, here we propose a methodology for crop type classification based on Sentinel-2 data focusing on a few specific aspects that have been often not addressed simultaneously in the previous works:

• Comparison of processing steps when single field representation is generated, i.e. multiple-pixel representation (Pixel-Set Encoder, https://doi.org/10.48550/arXiv.1911.07757) vs zonal statistics
• Application of various machine and deep learning classifiers, from Random Forest as a background, through Long Short-Term Memory and Gated Recurrent Unit, to novel Transformers
• Providing reliable full probability distribution of all crops for each classified crop field that allow for flexible user-specific minimalization of omission or commission errors
• Applicability in operational mode on a large beyond-country-scale i.e. for tens of millions of crop fields
• Efficient use of EO-data open cloud infrastructures such us the Copernicus Data Space Ecosystem or CREODIAS

The methodology has been already implemented and validated for Austria and Poland using Land Parcel Information System as a reference, but the list of countries will continue to grow. For Poland, 41 crops were successfully classified in 2022, for which the precision ranged from 0. 64 (pepper) to 0.97 (maize, beetroot, winter rape), and the overall classification accuracy (kappa) was 0.88. However, using a threshold of 0.85 on the probabilities allowed kappa of 0.95 and a range of accuracy (precision) for crops from 0.88 to 1.00, with 26 crops classified with an accuracy above 0.95. For Austria, for the years 2018-2021, a kappa coefficient of 0.85 to 0.9 was obtained without applying thresholds on probabilities. For Poland, classification was also carried out during the growing season reaching kappa of: 0.78 (in May), 0.84 (in June), 0.87 (in July) and 0.88 at the end of the season.

With this approach, we also demonstrate how to use the Copernicus Data Space Ecosystem to efficiently extract information from big volumes of satellite data - here from Sentinel-2 and tens of millions of agricultural parcels.

How to cite: Bojanowski, J., Musiał, J., Małaśnicki, J., Chojnacki, J., and Bylica, W.: Optimizing Country-Scale Crop Type Classification: A Comprehensive Study on Preprocessing Techniques and Deep Learning Classifiers within the Copernicus Data Space Ecosystem, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20031, https://doi.org/10.5194/egusphere-egu24-20031, 2024.

The EarthCODE vision will provide a cloud-based, user-centric development environment which can be used to support ESA science activities and projects. Our solution is built around existing open-source solutions and building blocks, primarily the Open Science Catalogue, EOxHub and the Exploitation Platform Common Architecture (EOEPCA+). EarthCODE capability will be hosted on the Copernicus Data Space Ecosystem (CDSE). Because EarthCODE will adopt a federated approach, it will also facilitate processing on other platforms, and these may include Deep Earth System Data Lab (DeepESDL), ESA EURO Data Cube (EDC), Open EO Cloud / Open EO Platform and perhaps AIOPEN / AI4DTE.

1. GOALS

EarthCODE will support FAIR Open Science. The main use cases will allow users to “Access Compute Resource” in the
cloud and “Conduct Science” using their preferred approach to create reproducible workflows. “Manage Workflows” will
allow users to execute workflows on a cloud platform of their choice and enable federated workflow and federated data
solutions. “Publish Results” will allow scientists to share experiments and data within the community and “Interact with Community” will enable a collaborative approach throughout this lifecycle. EarthCODE will allow scientists to concentrate
on science and collaboration and hide the complexity of Managing and implementing Workflows when required.

2. OPEN SCIENCE PLATFORMS
EarthCODE will provide users with a portal to the underlying platform services. Broadly speaking, this will include FAIR Open Science services that support the delivery of scientific workflows, Infrastructure Services that support the Execution of
workflows and Community Services to support underlying collaboration and exploitation.

3. BUILDING BLOCKS

EarthCODE will be created use existing building blocks. These will include EOEPCA+, EOxHub, Open Science Catalogue and other existing platform services.

4. PLATFORM FREEDOM

EarthCODE will be designed using open standards to help facilitate platform freedom and platform interoperability. This means that more than one type of Development Tooling can be used to conduct science and more than one of Integrated
Platform service can be used to Manage Workflows.

5. PLATFORM INTEGRATION
Using the above approach, EarthCODE will facilitate the use of data and services from a wide variety of platforms as illustrated
below. This shows how EarthCODE will promote integration with different platforms yet still provide freedom of platform
implementation. STAC will be used at the heart of the Science Catalogue to describe resources.

Platform integration will also be supported by a DevOps best practice approach to facilitate integration.

6. WORKFLOW AND DATA INTEGRATION

EarthCODE will prioritise the management of Workflow and Data modelling to ensure successful platform integration and
successful collaboration. This approach will further support the integration of platforms and data. FAIR Data principles will be
applied.

ACKNOWLEDGEMENTS

This work is funded by the European Space Agency.

How to cite: Smith, G., Glover, E., Kyle, A., Meißl, S., Debien, A., and Anghelea, A.: EarthCODE , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21722, https://doi.org/10.5194/egusphere-egu24-21722, 2024.

Nitrogen dioxide (NO₂ ) pollution is one of the most dangerous threats for public health. It is responsible for respiratory diseases, cardiovascular diseases, asthma, and many others. The objective of the presented study was to verify the potential of the Sentinel-5 Precursor Tropospheric NO₂ Column Number Density (NO₂ TVCD), provided by Copernicus Data Space Ecosystem, to support air pollution monitoring in Poland. The implementation of the objective was divided into three stages:

• to develop a model which estimates a ground-level NO 2 concentration;
• to find spatial representativeness area (SR area) referring to existing ground stations;
• to propose localization of new ground stations.

Model estimating a ground-level NO₂ concentration was developed in order to get spatial distribution of NO₂ pollution over whole Poland. As a input data there was used NO₂ TVCD provided by Copernicus Data Space Ecosystem as well as meteorological factors like air temperature, wind speed, planetary boundary layer height, pressure, atmospheric circulation type, wind direction, solar adiation flux from ERA-5 provided by European Centre for Medium-Range Weather Forecasts (ECMWF) and anthropogenic conditions (nightlight intensity, population, roads density). Due to the need for high computing power and a constantly working environment, CREODIAS resources were used. There were used several machine learning approach among which random forest had been found as a most accurate. The results revealed that model demonstrated MAE values of 3.4 μg/m³ (MAPE~37%) and 3.2 μg/m³ (MAPE~31%) for the hourly and weekly estimates, respectively.
Obtaining NO₂ ground concentration for whole Poland allowed for investigation of spatial autocorrelation of the air pollution phenomenon and determination of SR area. There were used five methods:

• Global and Local Moran’s: it was found strong spatial autocorrelation (Global Moran’s=0.99 and p-value <0.05). Also, ~1% of Poland NO 2 pollution does not depend on neighborhood area;
• Correlation in respect to distance: it was observed that c.a. 10% of Poland’s population exposed to high levels of pollution (higher than yearly World Health Organization - WHO recommendation=10 μg/m³ ) is not covered by a SR area;
• Semivariance: it was found that c.a. 12% of Poland’s population exposed to high levels of pollution is not covered by a SR area.
• Similarity threshold: It was found that c.a. 7% of Poland’s population exposed to high levels of pollution is not covered by a SR area.

Due to the findings above it was possible to determinate a number and localizations of the new
stations. Depending on method it was found there was a need to establish from 7 to 22 new stations
in order to cover all population threaten by high NO 2 concentration.

To sum up, implementation of Sentinel-5P data as well as meteorological and anthropogenic data
allowed for finding solution which could be very useful for design or improvement of air quality
measurements network.

How to cite: Grzybowski, P., Markowicz, K., and Musiał, J.: Support of air pollution monitoring using Senitnel-5P data – how to improve air quality measurement network , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22217, https://doi.org/10.5194/egusphere-egu24-22217, 2024.