@misc{10481/85011,
year = {2023},
month = {9},
url = {https://hdl.handle.net/10481/85011},
abstract = {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.},
organization = {Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of Spain},
organization = {PID2019-106195RB- I00/AEI/10.13039/501100011033},
publisher = {Taylor&Francis},
keywords = {Deep learning},
keywords = {DEMs},
keywords = {Void filling},
keywords = {Super-resolution},
keywords = {Landform classification},
title = {Deep learning methods applied to digital elevation models: state of the art},
doi = {10.1080/10106049.2023.2252389},
author = {Ruiz Lendiınez, Juan J. and Reinoso Gordo, Juan Francisco},
}