The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. This talk will summarize recent and ongoing efforts by the U.S. Geological Survey “Remote Sensing Coastal Change” project to effectively combine aerial and satellite remote sensing and Machine Learning-based image segmentation to observe and understand changing coasts, using imagery collected at a variety of scales. This work includes the development of the “Doodleverse”, a growing set of software tools, models, and data sets, that can be repeatedly repurposed for different measurement targets in the coastal zone, and well beyond.