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High resolution Digital Elevation Models(DEMs) are an important requirementfor many applications like modelling water flow, landslides, avalanches etc.Yet publicly available DEMs have low resolution for most parts of the world.Despite tremendous success in image super resolution task using deep learningsolutions, there are very few works that have used these powerful systems onDEMs to generate HRDEMs. Motivated from feedback neural networks, we propose anovel neural network architecture that learns to add high frequency detailsiteratively to low resolution DEM, turning it into a high resolution DEMwithout compromising its fidelity. Our experiments confirm that without anyadditional modality such as aerial images(RGB), our network DSRFB achievesRMSEs of 0.59 to 1.27 across 4 different datasets.

Feedback Neural Network based Super-resolution of DEM for generating high fidelity features