As climate change is increasingly affecting marine and terrestrial ecosystems, researchers, resource managers, and coastal communities are using satellite-based remote sensing, such as Sentinel-2 multispectral imagery, to monitor coastal environments at large scales. The ability to automatically define the position of the coastline from imagery, referred to as “coastline extraction”, is a valuable tool in extending monitoring of coastal ecosystems, such as kelp forests and eelgrass meadows, to regional and provincial scales. In this work, we present a new dataset for water segmentation, and thus coastline extraction, consisting of manually annotated Sentinel-2 images acquired at low tide, specific to the Pacific coast of British Columbia (BC), Canada. We then evaluate three methods for coastline extraction: an adaptive thresholding method, and two convolutional neural networks trained on firstly, a global dataset and secondly, our newly created BC dataset. The model trained on the BC dataset achieved the highest accuracy across standard image segmentation metrics and in coastline positional error measured relative to a manually defined reference coastline. Additionally, very-high resolution unmanned aerial vehicle data collected at validation sites with comparable tide levels to the Sentinel-2 dataset imagery showed that training on BC specific data decreases pixel misclassification, and therefore coastline positional error, due to the presence of subtidal and intertidal algae and vegetation at various validation sites in the study area.