This review discusses the definition and detection of shorelines, emphasizing the challenges and methods used in coastal research. Shorelines are dynamic boundaries between land and water, and their detection depends on the scale and context of the study. Common shoreline indicators include visually discernible features like the high-water line (HWL) and tidal datums such as mean high water (MHW). Recent advances in image-processing techniques allow for more objective detection of shoreline features from digital coastal images. Data sources for shoreline investigation include historical photographs, coastal maps, aerial photography, beach surveys, and remote sensing data. The identification of a shoreline involves selecting an indicator and detecting it within available data. Traditional methods rely on subjective visual interpretation, but modern techniques such as softcopy photogrammetry, LIDAR, and digital image processing offer more objective and repeatable results. Despite these advancements, challenges remain in defining and detecting shoreline indicators accurately. The spatial and temporal variability of shorelines means that different indicators may represent different aspects of the land-water boundary. The relationship between detected shoreline features and the physical boundary is still not fully understood, requiring further research into process-based definitions. The review highlights the importance of using appropriate data sources and detection methods to ensure accurate shoreline analysis. While objective detection techniques are now available, the challenge remains in developing a process-based definition that accounts for the dynamic nature of shorelines. Future research should focus on improving the accuracy and reliability of shoreline detection methods to better understand coastal change and management.This review discusses the definition and detection of shorelines, emphasizing the challenges and methods used in coastal research. Shorelines are dynamic boundaries between land and water, and their detection depends on the scale and context of the study. Common shoreline indicators include visually discernible features like the high-water line (HWL) and tidal datums such as mean high water (MHW). Recent advances in image-processing techniques allow for more objective detection of shoreline features from digital coastal images. Data sources for shoreline investigation include historical photographs, coastal maps, aerial photography, beach surveys, and remote sensing data. The identification of a shoreline involves selecting an indicator and detecting it within available data. Traditional methods rely on subjective visual interpretation, but modern techniques such as softcopy photogrammetry, LIDAR, and digital image processing offer more objective and repeatable results. Despite these advancements, challenges remain in defining and detecting shoreline indicators accurately. The spatial and temporal variability of shorelines means that different indicators may represent different aspects of the land-water boundary. The relationship between detected shoreline features and the physical boundary is still not fully understood, requiring further research into process-based definitions. The review highlights the importance of using appropriate data sources and detection methods to ensure accurate shoreline analysis. While objective detection techniques are now available, the challenge remains in developing a process-based definition that accounts for the dynamic nature of shorelines. Future research should focus on improving the accuracy and reliability of shoreline detection methods to better understand coastal change and management.