As the Very Large Array Sky Survey (VLASS) observes the entire Northern sky in radio, it generates quick-look images. These images often show residual artifacts, particularly around brighter sources. Most of these artifacts in VLASS are linear structures due to the VLA’s antennas mainly stretching in three lines and the snapshot nature of observations (VLASS often only has less than 5 seconds of data at a given location.) While well-established techniques (like CLEAN) maximize the information from snapshot imaging, these techniques can be imperfect. This incomplete image reconstruction is particularly true for surveys that prioritize the speed with which they generate large numbers of images to capture variable radio emission on fast timescales. Moreover, as surveys like VLASS become increasingly common, automatic image-quality classification is increasingly important for rapid data quality assessment and enabling the best science. For example, knowing whether a part of an image is affected by the above-mentioned linear streaks is critical for distinguishing truly variable radio sources from artifacts, among other science goals. Here I present new methods (e.g., prediction from the Fourier transform of the uv-sampling and empirical line detection using the Hough transform) for identifying residual linear structures in VLASS images. I also discuss the potential application of these methods to both on-the-fly and pointed observations with the VLA outside of VLASS and to observations with other interferometric arrays.