  We propose a new tiling strategy, Flip-n-Slide, which has been developed for
specific use with large Earth observation satellite images when the location of
objects-of-interest (OoI) is unknown and spatial context can be necessary for
class disambiguation. Flip-n-Slide is a concise and minimalistic approach that
allows OoI to be represented at multiple tile positions and orientations. This
strategy introduces multiple views of spatio-contextual information, without
introducing redundancies into the training set. By maintaining distinct
transformation permutations for each tile overlap, we enhance the
generalizability of the training set without misrepresenting the true data
distribution. Our experiments validate the effectiveness of Flip-n-Slide in the
task of semantic segmentation, a necessary data product in geophysical studies.
We find that Flip-n-Slide outperforms the previous state-of-the-art
augmentation routines for tiled data in all evaluation metrics. For
underrepresented classes, Flip-n-Slide increases precision by as much as 15.8%.
