The Cutting Edge project delivers an end-to-end web application for automated shoreline detection and quantitative change analysis tailored to Sri Lanka’s most vulnerable coasts. Users upload two raw Sentinel-2 satellite images, which are first validated by an image classifier to ensure they contain a shoreline view. Next, the system applies deep-learning segmentation—evaluating four state-of-the-art architectures (FCN8, U-Net, SegNet, and Deeplabv3+)—to generate precise binary masks of land–sea boundaries, selecting the highest-accuracy model for downstream analysis Group 19. Once segmented, the application computes two established metrics: End Point Rate (EPR), capturing the annualized rate of shoreline movement, and Net Shoreline Movement (NSM), indicating the total displacement between dates. These statistical models, driven by multi-temporal Sentinel-2 imagery spanning 2019–2023, enable robust identification of erosion and accretion hotspots and outperform purely manual or DSAS-based approaches by automating both image processing and trend forecasting Group 19. An integrated chatbot guides users through the analysis workflow, answering domain queries (e.g., “What is EPR?”) and interpreting results, while an image-validation component prevents erroneous inputs. The platform’s interactive visualizations and downloadable reports—including georeferenced maps of shoreline change—empower researchers, coastal managers, and policymakers with timely, evidence-based insights to inform conservation strategies, coastal engineering decisions, and community-focused adaptation measures. By streamlining complex image-analysis tasks into a user-friendly interface, this system significantly enhances the efficiency and accessibility of coastal monitoring efforts in Sri Lanka.