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Resiliants: WATER

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CoastAl Foresight

CoastAl Foresight is an AI-driven platform that leverages spatial computing and remote sensing to monitor and predict coastline changes. It provides critical foresight to enhance climate resilience for regions and municipalities, helping them prepare for and adapt to evolving environmental conditions.

Comprehensive Data Integration
Advanced Predictive Modeling
Real-Time Monitoring and Analysis

- Monitoring and predicting coastline changes - Enhancing climate resilience in coastal regions - Forecasting impacts of rising sea levels - Assessing risks of coastal floods - Analyzing effects of storms and high wind speeds on coastlines - Integrating diverse hydrospatial and geospatial data for comprehensive analysis - Providing actionable foresights for coastal management and planning - Facilitating data-driven decision-making for environmental conservation strategies - Supporting infrastructure planning in light of climate change impacts - Offering predictive modeling for proactive climate adaptation measures - Addresses: Coastal Floods, Storm/High winds speeds, Sea level rise

TRL2. Applied research: Technology concept and/or application formulated
At Technology Readiness Level (TRL) 2, CoastAl Foresight is in the technology concept and application formulation stage. This level indicates that the innovation is still in its conceptual phase, where the basic principles have been observed and reported, but it has not yet been developed into a functional prototype. To reach TRL 2, the following actions have been undertaken: 1. Concept Identification: The initial idea behind CoastAl Foresight has been identified, focusing on integrating AI and machine learning with hydrospatial and geospatial data to monitor and predict coastline changes. This concept arose from recognizing a gap in current coastal monitoring technologies. 2. Principle Observation and Reporting: The basic principles underlying CoastAl Foresight, such as data integration, predictive analytics, and real-time monitoring, have been theoretically established. These principles are supported by preliminary research, including literature reviews and theoretical analyses, to validate the feasibility of the concept. 3. Exploratory Research: Early-stage research has been conducted to explore the potential applications and implications of the technology. This includes identifying the types of data needed, the scope of AI applications in coastal monitoring, and potential challenges in implementation. 4. Initial Stakeholder Engagement: Discussions with potential stakeholders, such as maritime organizations and environmental agencies, have been initiated to gather input and assess the practical needs and challenges in coastal management. CoastAl Foresight is still in the early stages of theoretical development, with no functional prototype developed yet. The innovation is primarily supported by conceptual ideas and preliminary research, and extensive development and testing are required to progress to higher TRLs. Relevant studies: https://www.researchgate.net/publication/374122236_COASTSNAP_A_GLOBAL_CITIZEN_SCIENCE_PROGRAM_TO_MONITOR_CHANGING_COASTLINES_USING_SMARTPHONES https://www.researchgate.net/publication/328038132_Not_a_cape_but_a_life_preserver_the_importance_of_designer_localization_in_interactive_sea_level_rise_viewers https://www.researchgate.net/publication/365803626_The_future_of_coastal_monitoring_through_satellite_remote_sensing https://www.researchgate.net/publication/376529296_EXPLORING_THE_POTENTIAL_OF_OPEN-DATA_FOR_OCEANS_MONITORING_WITH_AI_ANALYTICS

See more information about this level and the TRL and SRL levels.

The system’s main components have been individually tested, and an initial integration has been completed.

Credits
CREDITS
Partner - Thomas Gkioulis
Collaborators
COLLABORATORS
No collaborators

How does it work?

CoastAl Foresight’s advanced data integration, predictive modeling, and AI-driven analysis make it highly pertinent to case studies focused on climate change impact monitoring, early warning systems for climate events, and smart monitoring technologies for marine and coastal environments. Its capability to analyze and forecast environmental parameters is a valuable asset for these specific challenges.

CoastAl Foresight is being developed by HARTIS Integrated Nautical Services Ltd, based on PhD research conducted at the National Technical University of Athens (NTUA). This development represents a direct application of academic research into a practical, innovative tool for coastline monitoring and climate resilience. The PhD research at NTUA, focusing on remote sensing and cartography, provided understanding into methodologies and technologies suitable for advanced coastal monitoring, including hydrospatial and geospatial data use. Building upon this research, HARTIS is transforming these academic concepts into a tangible product. As a company with expertise in nautical services and a member of the Open Geospatial Consortium (OGC), HARTIS is uniquely positioned to develop a tool that incorporates cutting-edge research while aligning with industry standards and practical usability. The development of CoastAl Foresight by HARTIS exemplifies the translation of academic research into a commercially viable and technologically advanced solution, adapting and enhancing NTUA’s research to meet the specific needs of coastal management and climate resilience planning.

As CoastAl Foresight progresses from its prototype stage, several factors shape its development and effectiveness. Satellite-Derived Bathymetry (SDB) plays a crucial role in data acquisition, providing a reliable source for necessary hydrospatial information. This approach addresses potential issues of data scarcity, ensuring a steady supply of quality data for analysis. Training data presents a notable challenge. The effectiveness of the AI algorithms within CoastAl Foresight hinges on the availability of comprehensive and diverse training datasets. Gathering such data, especially from less monitored or variable coastal regions, is crucial for the system’s accuracy and learning capabilities. Regarding adaptability to changing environmental conditions, the AI component of CoastAl Foresight is designed to learn and adjust to new patterns and anomalies. This adaptive ability is expected to enhance the tool’s performance in dynamic and unpredictable scenarios, making it a resilient solution in the face of environmental changes. Adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) principles is a foundational aspect of the system’s development. This commitment ensures that CoastAl Foresight will be compatible with a wide range of existing datasets and technological infrastructures, facilitating its integration into various coastal management frameworks. Maintenance simplicity is also a key focus in the development of CoastAl Foresight. The goal is to create a system that is not only technologically advanced but also user-friendly and easy to maintain, making it accessible to a broad spectrum of users, including those with limited technical expertise. This approach aims to maximize the tool’s applicability and longevity in diverse operational environments.

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