High-resolution Digital Elevation Models (DEMs) are essential for precise geographic analysis. However, obtaining high-resolution DEMs in regions with dense vegetation, complex terrain, or satellite imagery voids presents substantial challenges. This study introduces a deep learning approach using three-dimensional (3D) terrain features combined with Conditional Generative Adversarial Networks (CGANs) to reconstruct DEMs. The 3D terrain features, such as valley and ridge lines, exhibit topographic relief patterns and provide constraints for CGANs to reconstruct DEMs. Experiments conducted in the Loess Plateau of Shaanxi confirmed the performance of the proposed method, demonstrating marked improvements in the accuracy of DEM reconstruction compared to models based on two-dimensional (2D) terrain features. The elevation accuracy of the reconstructed DEMs by the proposed method is 5.30 m, which is higher than that of the 2D terrain features method (18.90 m) by 71.96 %. Meanwhile, the proposed method shows a 15.78 % and 17.64 % improvement in elevation accuracy and slope accuracy, respectively, when reconstructing a 5 m high-resolution DEM from a 30 m low-resolution DEM. The proposed method can be flexibly used for reconstructing, repairing, and filling voids in DEM data.

中文翻译：

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使用条件生成对抗网络从 3D 地形要素重建高分辨率 DEM


高分辨率数字高程模型 （DEM） 对于精确的地理分析至关重要。但是，在植被茂密、地形复杂或卫星影像空白的区域获得高分辨率 DEM 存在重大挑战。本研究介绍了一种深度学习方法，该方法使用三维 （3D） 地形特征结合条件生成对抗网络 （CGAN） 来重建 DEM。3D 地形要素（例如山谷线和山脊线）显示地形地貌模式，并为 CGAN 重建 DEM 提供约束。在陕西黄土高原进行的实验证实了所提出的方法的性能，表明与基于二维 （2D） 地形特征的模型相比，DEM 重建的准确性有了显著提高。所提方法重建的 DEM 高程精度为 5.30 m，比二维地形特征方法（18.90 m）高 71.96 m。同时，当从 30 m 低分辨率 DEM 重建 5 m 高分辨率 DEM 时，所提方法的高程精度和坡度精度分别提高了 15.78 % 和 17.64 %。所提出的方法可以灵活地用于重建、修复和填充 DEM 数据中的空白。