Coastal cities and ports play a crucial role in global trade, urban development, and environmental sustainability, but they are increasingly facing challenges related to climate change, urbanization, and complex operational demands. Within the framework of the OCEANIDS project, this study explores the integration of satellite imagery, climate data, meteorological and socioeconomic indicators, and artificial intelligence (AI) methodologies to improve the monitoring and management of coastal and port environments. By leveraging multi-source Earth Observation data and advanced machine learning techniques, a systematic approach is developed for environmental monitoring, operational assessments, and the detection of critical environmental changes. This integrated approach incorporates explainable AI techniques and data fusion methodologies to improve decision transparency, predictive accuracy, and operational planning. The implemented methodologies have resulted in actionable insights for managing urban growth, optimizing port operations, and mitigating environmental risks. End-user feedback from pilot sites in the Mediterranean, Boreal, and Atlantic regions highlights shared priorities, including wind forecasting, coastal changes, and sea level rise monitoring, as well as region-specific needs such as landslide risk assessments in the Azores and coastal changes monitoring in Malaga. The results underscore the importance of combining satellite data with forecasting models, predictive analytics, and GIS-based tools to support navigation safety, environmental monitoring, and climate risk management. A Decision Support System shall provide opportunities for scenario evaluation and policy development. This work establishes a scalable framework for sustainable coastal and port management, directly contributing to international sustainability objectives, including the European Green Deal and the United Nations Sustainable Development Goals.
Marinou et al. (Fri,) studied this question.