![]() It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased. A dataset with different pairs of digital elevation models was created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained. The development of a GAN-based methodology enables the improvement of the initial spatial resolution of low-resolution images. Here we address this limitation by the utilization of deep learning algorithms coupled with SISR techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. ![]() While global models of low and medium spatial resolution are available open source by several space agencies, the high-resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Digital elevation models are responsible for providing altimetric information on a surface to be mapped.
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