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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

With the development of artificial intelligence techniques for geographical knowledge discovery, simulated terrain generation based on deep-learning algorithms has become one practical way to construct accurate terrain data. However, it is still necessary to discuss whether the simulated topographic data contain the characteristics of specific landforms and can support related geographical studies. Therefore, in this study, a deep learning-based model inspired by previous research is constructed to generate loess landform data. We analyzed the influence of inputting different topographic features on terrain generation and evaluated the similarity between the simulated and reference data. The results show that the deep learning-based model can generate simulated topographic data that include similar elevation and slope probability distributions to the reference data of the loess landform. In addition, the generated results may have inaccurate terrain details, which can be regarded as noise in some cases. This indicates that the selection of input features should be carefully considered. Finally, the simulated data can subsequently support landform and terrain research, especially with intelligence algorithms that require large sets of topographic data.

Details

Title
Generating Terrain Data for Geomorphological Analysis by Integrating Topographical Features and Conditional Generative Adversarial Networks
Author
Li, Sijin 1   VIAFID ORCID Logo  ; Li, Ke 1 ; Xiong, Liyang 1   VIAFID ORCID Logo  ; Tang, Guoan 1 

 School of Geography, Nanjing Normal University, Nanjing 210023, China; [email protected] (S.L.); [email protected] (K.L.); [email protected] (L.X.); Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China 
First page
1166
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2637783987
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.