Topo2vec: Topography Embedding Using the Fractal Effect
This work addresses a domain-specific problem in geology by providing a novel representation for topographic images, which is incremental as it extends existing self-supervised techniques.
The paper tackled the lack of success in deep learning for geology by introducing a self-supervised learning method that exploits the fractal effect in remote-sensing images, demonstrating effectiveness on elevation data with improved classification tasks across different scales.
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.