Semantic segmentation, the task of assigning a class label to each pixel within a given image, has applications in a wide variety of domains, ranging from medicine to self-driving vehicles. One successful deep neural network model that has been developed for semantic segmentation tasks is the U-Net architecture, a "U"-shaped neural network initially applied to segmentation of cell membranes in biomedical images. Additional variants of the U- Net have been developed within the research literature that incorporate new features such as residual layers and attention mechanisms. In this research, we evaluate various U-Net-based architectures on the task of segmenting the road and non-road in low-altitude UAS visible spectrum imagery. We show that these models can successfully extract the roads, detail a variety of performance metrics of the respective networks' segmentations, and show examples of successes and pending challenges using U.S. Army ERDC imagery collected from a variety of ight routes and altitudes in a complex environment.