  Recent advances in deep learning have transformed many fields by introducing
generic embedding spaces, capable of achieving great predictive performance
with minimal labeling effort. The geology field has not yet met such success.
In this work, we introduce an extension for self-supervised learning techniques
tailored for exploiting the fractal-effect in remote-sensing images. The
fractal-effect assumes that the same structures (for example rivers, peaks and
saddles) will appear in all scales. We demonstrate our method's effectiveness
on elevation data, we also use the effect in inference. We perform an extensive
analysis on several classification tasks and emphasize its effectiveness in
detecting the same class on different scales. To the best of our knowledge, it
is the first attempt to build a generic representation for topographic images.
