Deep Learning-Based Super-Resolution of Digital Elevation Models in Data Poor Regions.

摘要: In order to develop reliable models, the geoscientific community requires high-resolution data sets. However, the collection of such data is a persistent challenge due to the limitations of resources. The concept of super-resolution, a method from the field of machine learning, can be used to predict a high-resolution version of a low-resolution dataset to improve usability in geoscientific applications. However, thus far, super-resolution is predominantly used in image data with few cases on improving the scientific data but focusing on improving quality of same downsampled data. More importantly, it is unknown whether models that are developed and trained with high-resolution data of specific locations can also be applied to data-poor regions. To address these gaps, this study investigated the use of deep learning-based super-resolution to improve the resolution of digital elevation data, focusing on the question whether models trained with high resolution data can also be applied to regions for which only low-resolution data are available. We focused on Digital Elevation Models (DEMs), as these are among the most important datasets for many geoscientific applications and used two of the most advanced Super-Resolution models (EBRN and ESRGAN) from different groups of deep learning architecture. We trained those models extensively using high-resolution LiDAR DEM data from Austria, and found that, for the Austrian study sites, these models performed better than commonly used interpolation techniques such as bicubic interpolation. To test model applicability to different terrain conditions, we applied the models developed and trained …