Deep learning methods applied to digital elevation models: state of the art

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.

Automated summary

Deep learning has been widely used in various domains, including spatial information and Digital Elevation Models (DEMs). This study reviews the methods of applying deep learning in altimetric spatial information and specifically in DEMs, such as void filling, super-resolution, landform classification, and hydrography extraction. Although these methods have great potential, there are still challenges that need to be addressed, such as improving terrain information or algorithm parameterization.