Generating Terrain Data for Geomorphological Analysis by Integrating Topographical Features and Conditional Generative Adversarial Networks

Sijin Li,Liyang Xiong,Ke Li,Guoan Tang

doi:10.3390/rs14051166

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.

Highlights

Four Terrain-conditional generative adversarial networks (CGANs) were trained using different combinations of topographic feaFour Terrain-CGANs were trained using different combinations of topographic features, and we compared their performance in four areas
A deep learning (DL)-based algorithm called Terrain-CGAN inspired by a previous study was constructed to generate topographic data of the loess landforms based on topographic feature
The positive terrain area, which is a special unit existing in loess landform areas, significantly improved structed to generate topographic data of the loess landforms based on topographic feature the accuracy of lines, simulated results

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Terrain is the fundamental object that significantly influences the geomorphic, hydrologic, and ecological processes of the Earth’s surface [1,2]. Various topographic characteristics carried by different surfaces provide information for landform classification, environmental evolution, terrain analysis, and hydrological analysis [3–6]. The digital elevation model (DEM) plays an important role in transferring geographical knowledge to computer-processable information [6]. It achieves a reliable way to represent the surface and supports the development of sophisticated techniques for geographical research [7–10]

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Conclusion