The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits. The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. VERSION 4 OF THE PRODUCT IS OPEN ACCESS, PLEASE CHECK THE LATEST VERSION Reference literature: Palomar-Vázquez, J.; Pardo-Pascual, J.E.; Almonacid-Caballer, J.; Cabezas-Rabadán, C. Shoreline Analysis and Extraction Tool (SAET): A New Tool for the Automatic Extraction of Satellite-Derived Shorelines with Subpixel Accuracy. Remote Sens. 2023, 15, 3198. https://doi.org/10.3390/rs15123198 J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez: Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Engineering, 2023, 104426, ISSN 0378-3839, https://doi.org/10.1016/j.coastaleng.2023.104426. Pardo-Pascual, J. E., Cabezas-Rabadán, C., Palomar-Vázquez, J., Fernández-Sarría, A., Almonacid-Caballer, J., Souto-Ceccon, P. E., Montes, J., Armaroli, C., and Ciavola, P.: Satellite-derived shorelines extracted using SAET for characterizing the effect of Storm Gloria in the Ebro Delta (W Mediterranean), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9856, https://doi.org/10.5194/egusphere-egu22-9856, 2022. Additional information, instructions and the code are available also in GitHub https://github.com/jpalomav/SAET_master Description of the product The product includes two different files: the Shoreline Analysis and Extraction Tool, SAET V 2.0, and the accompanying report. The report describes the state of the art of shoreline extraction from both SAR and optical satellite images and defines the strategies followed to improve the existing extraction methods. The report includes the description of the already existing and the newly proposed extraction methodologies and their assessment in several sites under different morpho-sedimentary and oceanographic conditions. The report aims at providing the background information on the framework of the SAET algorithm, as well as serving as a guide for the users of SAET. This software for shoreline extraction constitutes the project milestone MS5 - Calibrated algorithms of the ECFAS project. The software was developed according to the specific requirements of ECFAS. The Shoreline Analysis and Extraction Tool, SAET V 2.0 is a software developed in Python language focused on the automatic extraction of shorelines from optical satellite imagery (Sentinel-2, Landsat-8 and Landsat-9). Its main algorithm is based on the shoreline sub-pixel detection approach, adapted from the SHOREX system (Pardo-Pascual et al. 2012 & 2018; and Sánchez-García et al., 2017). This software allows obtaining the pre-storm and post-storm shoreline, enabling to make a comparison and to detect the erosion impact of storms on low-lying beaches. This SAET Tool is made available under the GNU General Public License v2.0 or later. This Report is made available under the Creative Commons Attribution 4.0 International License. Disclaimer: ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided. This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211