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European cultural heritage (CH) is at risk, threatened by environmental processes strengthened by climate change and anthropogenic pressure. In particular, the slow (landslides, subsidence) and seismic (earthquakes) movements of the soil have a strong impact on the structural stability of our cultural heritage (CH). The actions to be carried out to protect and safeguard CH are in continuous development and this is where the STABLE (STructural stABiLity risk assEssment) project fits. STABLE concerns the design and development of a thematic platform, which combines structural stability models, simulation and damage assessment tools, advanced remote sensing, in situ monitoring technologies, geotechnical and cadastral data sets with the WebGIS application for mapping and long-term monitoring of the CH.The thematic platform, which is the final objective of the project, will therefore support the authorities responsible for the conservation of cultural heritage in the design and implementation of policies for monitoring, preserving and safeguarding our heritage. This will allow effective monitoring and management of CH to prevent or at least reduce the possible irreparable damages.STABLE will coordinate existing skills and research in a synergistic plan of collaborations and staff exchanges to offer a complete transfer of knowledge and training to researchers in the specific area under study.The development of the platform will be the strategy that scientists will have to follow to share and improve CH safeguard methods.It will serve professionals to apply the most advanced technologies in their fields.

Today’s remote sensing data and technologies offer the capability to effectively monitor diverse and challenging environments around the world, such as coastal river and riparian zones. Coastal riparian zones and river deltas usually demonstrate extreme coastline changes in terms of the extent of water coverage of inland territories due to flood events, low and high tides, the climate, specific environmental characteristics, etc. In this paper, we exploit freely available multispectral time series data for previous decades, utilizing Landsat missions in order to develop an open-source-based image processing pipeline for the extraction of the actual yearly average coastline status of riparian river delta areas. The latter present significant temporal coastline changes between years, semesters, and months. Average mean maps are generated and then compared to several temporal levels in order to distinguish long-term significant changes and ecosystem threats. Additionally, a custom long short-term memory (LSTM) neural network is deployed to forecast the evolution of the coastline by exploiting the average value for each pixel across all available images as a training sample and producing a forecast output for the next period. The network achieves accuracy scores of 89.77% over 'non-water' depicting pixels and 84.26% over 'water' depicting pixels, for regions that present frequent changes between land and water coverage over time. The predicted map presents high statistical agreement with the respective average map generated in two different validation approaches, with kappa coefficients of 85.9% and 91.4%, respectively.

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