Despite widespread adoption of deep learning models to address a variety of computational vision tasks, planetary science has yet to see extensive utilization of such tools to address its unique problems. On Titan, a moon of Saturn, tracking seasonal trends and weather patterns of clouds provides crucial insights into one of the most complex climates in the Solar System, yet much of the available image data is still processed manually. We demonstrate that transfer learning techniques can deliver a high degree of accuracy for cloud detection on the data collected from the Cassini–Huygens Mission to Saturn from 1997-2017. We present this work to encourage others to join us in analysis of cloud data throughout the Solar System, as future telescopy projects promise an influx of images in the coming years.