The project coordination will develop management processes and implement them for an efficient and successful project execution, following the PMBOK principles and best practices. The following tasks will be performed:

• Preparation of technical and financial interim reports in a six-month basis (see below) so that deliverables and milestones of each period are closely followed and so that technical activities follow resource consumption.
• Preparation of the external EC reports (intermediate in M18 and final report in M36) according to the EC guidelines, monitoring technical and financial activities as well as audit reports in applicable cases; Distribution of the EC financial contribution and costs coordination and controlling.
• Organisation of consortium meetings (project board, technical).
• Communication with EC and submission of deliverables to the EC.
• Ensuring the communication between partners at all levels and the proper information exchange.
• Support of the evolution of the work plan (time plan consistency, critical tasks highlighted), including in collaboration with T1.2 for technical matters.

This task will monitor and coordinate the technical progress of each WP in order to assure that the respective outcomes are in line with the needs of the other WPs and consistent with the project objectives. This procedure will involve the Project Technical Coordinator (TC) and the various WP Leaders (WPLs) where the latter are responsible for the corresponding deliverables and milestones. WPLs will provide periodic technical reports on a quarterly basis, identifying on a task by task basis, progress, deviations, challenges and issues. Technical Management will work to ensure the respect of technical objectives and deadlines, while coordinating to resolve critical technical issues. It will conduct physical or virtual meetings between the TC and the WPLs to provide oversight of work progress, work completion monitoring, and conformance to planned deadlines. Attention will be particularly paid to the WP resource utilization and WPLs’ commitment to the project's technical goals.

This task includes Quality Management, aiming to ensure that all deliverables and project procedures are compliant with quality objectives before they are issued. The quality assurance process will be defined and monitoring of milestones as well as KPIs (key performance indicators) will be performed. Deliverables including the progress reports deadlines will be controlled in order to be submitted to the EC on-time and with maximum quality. A Quality Management Plan (QMP) will be produced establishing a quality control and criteria strategy and identifying the quality objectives. This will include a peer review system consisting of representatives of each partner and quality criteria in the form of a methodology that will be followed through the project duration. A Risk Management Plan (RMP) will also be developed specifying in detail how risks are classified and handled. The RMP will classify all risks and present in detail the methods used to manage risks as well as relevant contingency measures. The resolution of risks will be based upon evaluation of technical progress, schedule and cost impact. The RMP will be kept under review during the project duration, updated in plenary meetings and at the end of each reporting period. RMP and QMP will be included in the EC reports (intermediate and final).

In this task, the data being handled throughout the project will be analysed to ensure its correct treatment according to legal frameworks. It will ensure that the data collected during the project are accessed, saved, shared, and reused following best practices and specific standards that will be identified and periodically included in a Data Management plan (see Sections 2.2.4, 5). Any personal data collected during the project’s participatory activities (WP2, WP7, WP8) will be compliant with GDPR, and, if applicable, national data privacy regulations and obtained through informed consent. Moreover, regarding secondary use of data from GEOSS or other sources, if and when such data comprises personal information (e.g. potentially socio-economic or VHR data), appropriate anonymization methods and aggregation methods will be applied so that the requested information for each CC application is obtained, yet, the specificities for a person and its associated information are not kept. Additionally, ethical and privacy protection will play an important role in ensuring compliance with the GDPR, especially within the scope of the project's pilots where work will be done in some cases with more sensitive information.

The visualisation (or UI/UX) engine of EIFFEL data exploration tools will offer map-based search or, equivalently, spatial search based on given coordinates, and a standard set of filtering criteria that legacy engines already provide, e.g. dataset year/timespan, data provider, format, scale, resolution. Beyond this, an important functionality is that the engine will dynamically show an advanced tree-view checkbox filtering structure, mobilized by both the EIFFEL ontology structure and the concept relations and keywords revealed via NLP. The engine will prioritise the returned results according to their ‘relevance percentage’. More importantly, a list of the most frequent similar searches performed by other EIFFEL cognitive data exploration users shall be presented, as well as a user-friendly and navigable list of relevant -external to GEOSS- datasets, that shall be returned from the T3.4. The tool explores the information without knowing the structure of datasets, taking advantage of the semantic relation among the defined terms. Technically speaking, the UI/UX will be implemented as a web-based tool permitting interoperability among digital systems and universal use, using a common data format (most probably JSON-LD). Moreover, the back-end of the engine will be cloud-based, supporting data storage, querying and retrieving. This will support full text search capabilities for metadata search as well as cosine similarity based direct search for vector matching in cognitive search querying in line with T3.3 and T3.4 requirements.

The result of this task will be the EIFF-O ontology and the associated semantic annotation tools and converters to be used in the EIFFEL pilots. This task will, first, analyse the current ontologies available in the GEOSS and CC fields, especially the latest OGC EO JSON-LD standard issued. Then, the different ECVs will be analysed and a “climate tagger” will be developed, allowing dynamic annotation based on clear rules. Third, the different syntactic and semantic characteristics of the different GEOSS datasets and other data sources present in EIFFEL pilots will be analysed, exploring significant overlaps in terminology and to establish components of a common ontology. This will be extrapolated in order to achieve an as-agnostic and as-scalable-as-possible ontology in the project. Afterwards, advanced semantic techniques will be used for the semantic representation by clustering knowledge, ensuring linked data compliance. Afterwards, EIFF-O will be developed following the guidelines established since the proposal stage. Then, the needed bridges for the pilots will be developed by the technical team. Finally, a set of guidelines, complete documentation on how to use the ontology will be created. This documentation will be made public and the associated API will be created.

This task has officially ended and you can access the output of the work done:

• Watch our YouTube tutorial <https://youtu.be/4maOkmRMnNE>
• Access the code in GitHub <https://github.com/benmomo/eiffel-ontology>
• Read the online documentation <https://eiffel-ontology-doc.readthedocs.io/en/latest/>
• See our online demo <https://eiffo.satrdlab.upv.es/>

This task will develop the EIFFEL cognitive search framework that will enable users to make multi-fashion queries and effectively discover rich GEOSS data content with emphasis to CC-related topics. A major aspect of this task is to enable the developed search engine with advanced semantic comprehension and human language understanding abilities. To achieve this the steps to be followed are: A) We will systematically scrap, collect and characterize vast amounts of relative unstructured document corpora including GEOSS metadata short descriptions, CC and geo-related open access scientific journals, relevant EC project deliverables, Wikipedia and websites linked to GEOSS data assets. B) we will employ unsupervised NLP tools and AI-enabled language models such as Deep bidirectional transformers to produce data-driven, context-aware word embedding specialised on geo-related and CC topics. C) explicitly incorporate domain-knowledge expertise via the EIFF-O (T3.2) and also enhance data-driven word-vectors with hard to detect semantic dependences. Having the steps above successfully fulfilled, the EIFFEL search engine will be built following hybrid intelligent data search and retrieval schemes exploiting ontology-based and AI/data-driven based search. For this task, document ranking and AI-based information retrieval schemes will be developed and applied on augmented GEOSS metadata of T3.4.

The aim of this task is to develop a range of metadata enrichment and curation mechanisms that will automatically augment the metadata of existing GEOSS datasets as well as of datasets that will be produced (by any source or actor) during the project duration and beyond. In particular, within this task we will develop: A) A metadata keyword generator and augmenter that for each relevant GEOSS data asset will consume information of its metadata, short description and content in linked sources and it will generate keyword lists exploiting the semantic and concept associations revealed via the AI-enabled GEOSS-aware language model developed in T3.3. Moreover, existing keyword lists will be enriched with new terms and relevant concepts via linking each asset into domain-specific concepts, and hierarchies underlining EIFF-O produced in T3.2. Finally, existing keywords will be paired with novel ones by means of automatic classification of the asset metadata in concepts, key-terms and synonyms carrying domain knowledge information whenever possible. B) we will develop automatic information retrieval schemes that will extend metadata with relevant external information from a diverse set of sources such as local geospatial data, Corine, LUCAS, information about research works and projects using the data assets, and relevant data from other disciplines, i.e. socio-economic, and sources (e.g. statistical authorities, custodian agencies, national/international registries), which are sparsely available in the portal. C) We will develop dedicated ML models and data analytics tools in order to extracts valuable insights from the raw data themselves, such as statistics, analytics, extreme values, data quality assessments, trends, anomalies etc. Then the metadata will be augmented with such impactful information rendering it searchable by the EIFFEL cognitive search engine. Metadata enrichment, augmentation and extension will be realized dynamically in metadata database, which will not be physically linked with the raw data themselves rendering the generated metadata active allowing progressive augmentation in regular phases as long as new algorithmic developments and new relevant info comes into place.

This task is responsible for providing not only the raw sentinel data to the different pilot but also to provide pre-processing products fine-tuned based on pilots’ requirements. This action will be closely supported from pilot leaders providing both requirements and feedback. The application builds on top of the Sentinel Hub application developed by NOA and provides added value service to pilot leaders in order to not be limited and/or concerned about “where to acquire”, “how to process” and “extract” the needed information from raw satellite data but to only get the required information in the agreed frequency, area, resolution etc. On top of the abovementioned, that is an operational service, this task will also apply research on how to combine/blend sentinel data with datasets from GEOSS portal. GEOSS portal is not providing access to data but acts as a broker to data providers. Therefore, NOA will try to showcase how users can extract the needed information from Sentinel hubs (e.g. Sentinel 1/2/3/5p images) based on a condition that is related to other data sources from GEOSS portal. An example of that could be a blend of weather and/or land use data with sentinel 2 images, thus responding on the following request: “Retrieve all Sentinel 2 images from 2019 for a specific area in which weekly accumulated precipitation was below 50mm” This could result in providing the needed batch of datasets and not the whole archive of 2019. The operational service is assigned to EDGE and the research task will be NOA’s responsibility. The service itself will be hosted either on a DIAS provider or on a cloud provider (e.g. GRNET).

Sentinel Hub App

The enhanced Sentinel Hub App fulfils a well-defined user need: uniform Application Programming Interface (API) access to Sentinel 1, Sentinel 2, Sentinel 3, and Sentinel 5p metadata via back-end connection to a number of data sources such as national mirror sites, DIAS platforms, etc. Umbrella is not connected only to DHuS-based platforms but is platform-independent. This unique access point provides users with all the required Sentinel metadata related to their requests via an API, allowing the implementation of a transparent single data access point. Ultimately, this application gives the potential for: 1. linking federated Copernicus Sentinels Hubs to a single data hub instead of searching for the most appropriate for the user’s needs; 2. accessing all Sentinel mission data and better performance on downloading products, as products are chosen from the most appropriate data hub based on integrity, speed and availability tests. 3. data filtering using GEOSS meteorological datasets, thereby decreasing returned result volume.

Website: http://umbrella.beyond-eocenter.eu
Web App: http://umbrella.beyond-eocenter.eu/gui
Youtube tutorial: https://www.youtube.com/watch?v=Iy4CvVz-4Vs
Documentation: https://www.eiffel4climate.eu/images/deliverables/EIFFEL_D3.5_v1.0.pdf
Public demo: http://umbrella.beyond-eocenter.eu

The deliverables of EIFFEL project are listed per Work Package. Following their final approval by the European Commission, you can download the deliverables since they are public in terms of dissemination level.

D3.2 Report on the EIFFEL Ontology

D3.5 Report of new features of the Sentinel Hub App

This task will focus on developing the modelling and DSS for assessing the impact of adaptation measures on the water and soil carbon conditions in the Aa river basin in the Netherlands. The hydrological/water management models will be developed in a detailed scale for testing measures on (groups) of individual properties/fields, while the soil carbon model will be on a major land use changes. ML emulators of these models will be set up, in close collaboration with T6.1, as needed for the DS components that will be used by identified stakeholders. Separate models will be developed for the Belgian part of the Aa river basin, using GEOSS data to assess the potential of such applications in cross-boundary river basins. Detailed requirements for the models will be obtained from the work of WP2 (especially Tasks 2.1, 2.2). The DSS will be web-based that will allow presentation and testing of the adaptation measures by different stakeholders in a user-friendly way.

This task will develop the data framework and mining tools for a consistent land representation system and for efficient estimation of carbon stock changes. The latter will be used by farmers and policymakers to manage local and transnational microclimate effects more efficiently. The pilot application involves a set of novel ensemble models, i.e. multi-input CNNs and XAI models from T6.1 for the accurate (downscaling them at field level) information for operational DS and reporting of GHGs procedures. Carbon monitoring in agro-ecosystems shall be highly enhanced, integrating existing physical process models, with in-situ datasets (e.g. Soil spectral libraries, sensors) and multi-resolution data flows from Sentinel 1,2 in T4.2. The application is a solid demonstrator of the value that can be achieved with the EIFFEL tools. Its importance and impact on the farming sector is substantial, as they support optimisation of the practices of a climate-smart agriculture (e.g. crop diversification), utilizing big data sources (crop metrics), future climatic scenarios (RCP scenarios) and land suitability analysis tools.

This task will implement a CC advanced data analytic tool and DSS, that capitalizes on data mining and AI techniques for the analysis, monitoring and prediction of pollution episodes and, consequently, for the planning and optimization of port activities. The latter include cargo, load/unload operations, cruise ships and land traffic. The overall aim is to quantitatively assess the CC impact of port activity, taking into account the requirements and specifications of T2.1-2.2. This implementation considers the following steps: (i) full integration with EIFFEL tools to acquire Sentinel data at regional level and other in-situ and external datasets at local levels (T3.5 and T4.2); (ii) building predictive models of pollution episodes and traffic congestion based on different AI techniques, e.g. data mining, pattern and anomaly detection, calibration, extrapolation etc.; (iii) using XAI models to improve the interpretability of the application (adapting and applying XAI models from T6.1 to the pollution and traffic models); (iv) quantification and presentation of results in a user-friendly DSS, comprising dashboards with KPIs.

Driven by WP2 (incl. geographic upscaling), four urban CC applications will be developed. Compilation of necessary data with explicit requests for particular socio-economic data is implemented, using relevant EIFFEL tools of WP3, WP4 and the respective datasets. The work will include: 1) Creation of an enhanced Building Stock Model (BSM) utilizing EO and socio-economic data. Assessment of GHG mitigation scenarios using the BSM in the selected LAU; 2) Exploitation of SENSE to derive the energy potential of the Attica region and calculation of solar energy availability for the selected LAU at building level. Identification of potential areas for PV penetration and estimation of GHG abatement equivalent; 3) Utilization of the COPERT methodology to simulate GHG mitigation scenarios, exploring vehicle fleet composition and alternatively fuelled vehicles. Assessment of securing emobility energy from buildings; 4) Air quality, city-scale modelling for quantifying the effects of the above scenarios on pollutant concentration and citizens’ exposure, bundled in a user-friendly DSA.

In this task, we will design and develop a CC-oriented application that will implement a framework for multi-hazard risk assessment, as described in the Pilot 5 part of Section 1.3.1.3. The framework will assess and quantify several biotic and abiotic forest disturbance factors, capitalising on augmented Sentinel- and local data sets (WP3, WP4) and will produce hydrological, fire risk and pest risk models on remotely sensed data, seasonal weather forecasts and long-term climate projections. The analysis will provide novel uses of GEOSS data in a modelling framework at regional and national scale, by building predictive models of forest pest occurrence and the quantification of present-day and future risks of drought and forest fire risk based on impact response surfaces (see 1.3.1.1). These will be the basis for upscaling regional analysis of P5 to a European-scale assessment in Task 6.3.

This task will coordinate the assessment of pilot applications including the stakeholder engagement (together with T8.2). The results of the intermediate and final assessments of pilot demonstrations will be captured in a structured way and jointly interpreted against objectives (KPIs, starting from Table 1 and elaborated together with pilot partners and CoPs stakeholders within this task) and user needs (T2.1). The contributions to policy goals and recommendations from pilots, including operational aspects, benefits and gaps for the integration of GEOSS data and exploration tools, best practices and lessons learned (T7.2-7.6) from national or sub-national perspective, will be compiled and synthesized in a systematic way.

In Pilot 1, we run a co-design project on planning and implementing nature-based solutions (NbS) for ‘climate proofing’ in the Aa of Weerijs catchment, using the DS components developed in T5.1. The pilot will address the main challenge for evaluating and proposing new NbS. The impact of potential measures focusing on water management, land use and soil carbon changes (see 1.3.1.3) will be assessed. In a first step, we will activate CoPs stakeholders onboard since the T2.1 focus groups, for joining testing of the applications (T5.1). Together with these stakeholders, we will organize demonstration and testing workshops. User experiences, suggestions for improvement and specification requests will be channelled back to T5.1 and T6.3 for the upscaling part. Special emphasis will be put on the part of the application for cross-border cooperation (NL-BE) in the catchment and monitoring and modelling both climate effects and impacts of measures.

This pilot demonstration will evaluate the overall performance of the Pilot-2 components through complete end-to-end scenarios deployment within a co-design procedure led by NPA. The following activities will take place: (i) Identification of representative pilot studies within Lithuania based on stratification with an active involvement of up to 400 farmers; (ii) Evaluation of the Agricultural Carbon Hybrid Model in support of national GHG inventories by moving to higher Tier methodologies; (iii) Full-scale deployment of the future land suitability tool mapping along with the recording of the first perceptions about performance, usefulness, and immediate or potential benefits by local farmers. The demonstration will address the main challenges identified by the paying agency, offering a novel solution to the monitoring, reporting and verification of LULUCF and to farmers a set of climate smart agricultural services for farming sustainability.

The pilot will focus on the monitoring and prediction of pollutions episodes at BPA and the planning and optimization of port activities (cargo, load/unload operations, cruise ships and land traffic). It will provide a real test-bed for better decision making by minimizing the carbon footprint of the port activity and the impact on the city. The task will test and evaluate the advanced analytical tool and DSS developed in T5.3 in absolute terms (effectiveness towards technical KPIs), in terms of operational KPIs, and also environmental and societal KPIs for the port, city and citizens as a whole, including contributions to policy goals (PA, SDGs), recommendations for best practice and overall exploitation opportunities (input to WP8). This pilot will be evaluated in two iterations. The first one (M28-M30) will validate the complete version of the advanced analytical tool (D5.3) and will provide feedback for the final optimization and changes to it in T5.3, up to M30, while the second one (M31-M34) will provide final conclusions and assessment.

In Pilot 4, the added value of the application for inspecting urban GHG mitigation scenarios for building energy efficiency, photovoltaic penetration and vehicle fleet emissions (Task 5.4) will be demonstrated at two levels: in the selected LAU and at the whole Region of Attica. In the LAU, the DSA, as its components, will be tested in full force, deployed in high spatio-temporal resolution, and higher tier tools for mapping and linking the different sectorial layers shall be utilized. The upscaling from the municipality to regional level will be implemented via i. products available at the regional scale, ii. projection of outcomes from the pilot to the whole region, the latter taking into account the characteristics of the sub-areas and local knowledge. The results from the DSA, with respect to the abatement of GHG per sector and overall, will be incorporated into the Regional Plan for Adaptation to CC of Attica and taken into account in its implementation. Transferability in other LAUs will be explored and the impact on achieving the National Plan for Energy and Climate from the Greek State, will be quantified.

The framework for multi-hazard risk assessment of several biotic and abiotic forest disturbances, developed in T5.5, will be tested and verified with stakeholders. Data exploration (WP3) and augmentation (WP4) tools will be tested and the benefits of integrating GEOSS data in the modelling framework will be evaluated. Feedback on development needs/gaps will be provided to those WPs. Hydrological, fire risk and pest risk models will be applied for identifying areas prone to drought, forest fires and pest in Finland. Future risks of drought, forest fires and pests based on climate scenarios will mapped. The intermediate and final mapping results will be presented to and co-interpreted with stakeholders and alternative adaptation measures will be discussed. Final maps on present-day and future risks of forest disturbances will be presented in the Finnish climate guide and seasonal/long-term drought forecasts will be integrated in the national portal for water resource information. Recommendations for priority research on forest disturbances and actions for adaptation plans in Finland will be given.