Critical information about surface water bodies, particularly their dynamic behavior, is most effectively derived from water contour detection. However, the accurate detection of contours is complicated by the land–water ambiguity and the great imbalance between contour and non-contour data. A unique fully convolutional multiscale UNet-styled (MS UNet) deep network is proposed for accurate water contour detection in the visible spectrum. The MS UNet utilizes blocks of multiscale convolutional filters to improve contour detection and employs loss functions to correct the imbalance between contour and non-contour data, as well as capture the loss at both the pixel and object levels. The proposed system is shown to be more effective at detecting water contours than recent water detection systems and other popular image segmentation networks while using a fraction of the parameters.