Periodic Reporting for period 1 - OCEANIDS (User-driven applications and tools for Climate-Informed Maritime Spatial Planning and integrated seascape management, towards a resilient & inclusive Blue Economy)
Período documentado: 2023-12-01 hasta 2025-05-31
Coastal regions are often characterised by strategic socio-economic assets. This makes coasts particularly sensitive to Climate Change (CC) impacts, which primarily expose infrastructure and local population. Human activities are also responsible for additional pressures on coastal ecosystems, often generating more immediate impacts than those expected from CC by aggravating existing vulnerabilities. The need for CC adaptation in coastal areas is evident and is predicted to become progressively more significant over time due to the grim long-term forecasts of climate variables. Coastal area adaptation strategies should be iterative and dynamic, due to the evolving dynamics of coastal territorial systems. Furthermore, CC adaptation measures should consider local ecology, economy, society, politics, and technology. Therefore, the definition of Climate Adaptation Planning (CAP) must consider specific local socio-economic contexts. The OCEANIDS project aims to develop the tools and applications that enable a more resilient and inclusive society in coastal regions via better-informed and integrated seascape management. The central concept is to collect, harmonise, and curate existing climate data services, making data accessible, reusable, and interoperable for developing local adaptation strategies.
Strategic Objectives are:
Improve access to existing data and services via application related to CC-induced impacts on the coastal region
Create beyond State-of-the-Art re-usable information tools tailored to users’ needs
Novel climate services, fully integrated into operational EU infrastructure, to be further used by the Mission
Providing a data-exchange framework that will allow efficient flow and validation of data from both local and central sources
Demonstrate the OCEANIDS tools and application by 12 End-Users in 7 EU different regions, ensuring vulnerable and challenged regions are represented
Scientific and Technological Objectives are:
Identify all key stakeholders related to the final tools and applications of OCEANIDS
Perform an in-depth gap analysis of the root causes of poor data consumption and application uptake by regional stakeholders
Social innovation - Develop a citizen engagement framework allowing multi-participatory and co-creation, towards a more inclusive society
Identify the use of New EO data services requirements & specifications
Climatic models, forecasting & Meteorological models curation & quantitative environmental impact assessment in coastal regions
Develop a platform for assessing the impacts and risks of climate change on key community systems, as highlighted in the Mission Implementation Plan, including multi-hazard assessment
Develop an integrated EO and spatial data platform (single-access window) for CC-related data and services for regional stakeholders
Develop a DSS towards enabling CI-MSP in coastal regions
Deploy the OCEANIDS tools, modules, applications and platforms on operational infrastructure in a modular, reusable and user-friendly way
Ensure project results replication in other regions, dissemination results of results to a wide audience and sustainability of the approach in real-life conditions
Liaising with other EU projects & initiatives & lessons learnt
Strategic Objectives are:
Improve access to existing data and services via application related to CC-induced impacts on the coastal region
Create beyond State-of-the-Art re-usable information tools tailored to users’ needs
Novel climate services, fully integrated into operational EU infrastructure, to be further used by the Mission
Providing a data-exchange framework that will allow efficient flow and validation of data from both local and central sources
Demonstrate the OCEANIDS tools and application by 12 End-Users in 7 EU different regions, ensuring vulnerable and challenged regions are represented
Scientific and Technological Objectives are:
Identify all key stakeholders related to the final tools and applications of OCEANIDS
Perform an in-depth gap analysis of the root causes of poor data consumption and application uptake by regional stakeholders
Social innovation - Develop a citizen engagement framework allowing multi-participatory and co-creation, towards a more inclusive society
Identify the use of New EO data services requirements & specifications
Climatic models, forecasting & Meteorological models curation & quantitative environmental impact assessment in coastal regions
Develop a platform for assessing the impacts and risks of climate change on key community systems, as highlighted in the Mission Implementation Plan, including multi-hazard assessment
Develop an integrated EO and spatial data platform (single-access window) for CC-related data and services for regional stakeholders
Develop a DSS towards enabling CI-MSP in coastal regions
Deploy the OCEANIDS tools, modules, applications and platforms on operational infrastructure in a modular, reusable and user-friendly way
Ensure project results replication in other regions, dissemination results of results to a wide audience and sustainability of the approach in real-life conditions
Liaising with other EU projects & initiatives & lessons learnt
Firstly, the main activities implemented in the OCEANIDS Project focus on the development of the Stakeholder Plan and user needs identification aiming to create an Inventory of available services and local needs and faced challenges. This has been achieved by interviewing local and regional authorities starting within the consortium and expanding during the project.
Secondly, a preliminary overview of the main modules and workflows developed under WP3 and the requirements from a technical perspective, but not exclusively limited to that were identified and documented. It is essential to maintain an integrated approach considering that the core technology modules created under WP3 will be ultimately integrated into the platforms generated under WP4. Therefore, these requirements were also extended to the introduction and description of the final platforms developed under WP4.
Secondly, a preliminary overview of the main modules and workflows developed under WP3 and the requirements from a technical perspective, but not exclusively limited to that were identified and documented. It is essential to maintain an integrated approach considering that the core technology modules created under WP3 will be ultimately integrated into the platforms generated under WP4. Therefore, these requirements were also extended to the introduction and description of the final platforms developed under WP4.
OCEANIDS represents a pioneering initiative poised to revolutionize the utilization of satellite data alongside auxiliary sources from GEOSS and various other channels. By harnessing this wealth of information, OCEANIDS aims to elevate the precision and reliability of analysis while concurrently establishing an interoperable framework. The ultimate objective? To expedite the delivery of Earth Observation (EO) data with enhanced efficiency and flexibility. At the heart of OCEANIDS lies a comprehensive approach to quantifying the impacts of climate change (CC) in coastal regions. Through meticulous identification, federation, and curation, the project seeks to pinpoint a select set of primary parameters and impact indicators. These metrics, ranging from climate extremes to hydrological factors and stress indicators, will be meticulously chosen based on a reliability-centric methodology, ensuring durability and sensitivity. By providing end-users with a streamlined data service, OCEANIDS aims to eliminate the clutter and complexity often associated with data analysis. Leveraging cutting-edge advancements in image analysis and machine learning, OCEANIDS empowers the processing of multi-dimensional image cubes with unparalleled sophistication. From dimensionality reduction to shadow detection and compensation, and even deep learning models for semantic characterization, the project employs a diverse array of methodologies. Tensors-based learning further augments the analysis, particularly beneficial when labelled data samples are scarce—a common challenge due to the substantial effort and time required for acquisition.