Addressing the impact of sea-level rise in coastal areas requires sustainable management strategies based on informed decision-making, and thus a shoreline projection is important. This research explores methods to improve shoreline change projections by obtaining a long-term shoreline change trend, empirically from \acrfull{sds} observations, and implementing a coastal impact model to project the future shoreline. We defined a heuristic approach to obtain a clean-\enquote{primary}- shoreline-change signal from all raw \acrshort{sds} observations that are available at transect. Long-term trend analysis from the primary shoreline-change signals results in a more accurate long-term shoreline-change trend. The improvement is also observed in Truc Vert, a meso-tidal case previously well known for low \acrshort{sds} accuracy, where the primary series results in accurate long-term shoreline trend regimes. Our projections in open sandy transects show that long-term trends predominantly influence shoreline change patterns and regimes compared to the retreat due to sea level rise. Using input comparable to the input of an earlier study, projections in seven out of eight regions indicate less retreat, except in North Carolina where the regional sea-level rise projections are relatively high. Compared to a hybrid, physics- and process-based, model, our projections showed higher progradation and erosion in \enquote{hot spots}, revealing limitations in linear trend extrapolation for capturing non-linear shoreline behaviour. We can conclude that future shoreline-change projections can be improved by using more accurate long-term shoreline-change trends. The framework's applicability is best suited to open sandy beaches with minimal human intervention. This highlights the need for better coastal typology classification that formalizes the concept of coastal type and coastal squeeze in the backshore of the beach. The outlook for the development of the shoreline projection framework is to explore the non-linear behaviour of shoreline change. This can be captured by decomposing the primary shoreline-change series into different signals that better represent the shoreline-change behaviour.