Abstract
Digital elevation model (DEM) interpolation is aimed at predicting the elevation values of unobserved locations, given a series of collected points. Over the years, the traditional interpolation methods have been widely used but can easily lead to accuracy degradation. In recent years, generative adversarial networks (GANs) have been proven to be more efficient than the traditional methods. However, the interpolation accuracy is not guaranteed. In this paper, we propose a GAN-based network named gated and symmetric-dilated U-net GAN (GSUGAN) for improved DEM interpolation, which performs visibly and quantitatively better than the traditional methods and the conditional encoder-decoder GAN (CEDGAN). We also discuss combinations of new techniques in the generator. This shows that the gated convolution and symmetric dilated convolution structure perform slightly better. Furthermore, based on the performance of the different methods, it was concluded that the Convolutional Neural Network (CNN)-based method has an advantage in the quantitative accuracy but the GAN-based method can obtain a better visual quality, especially in complex terrains. In summary, in this paper, we propose a GAN-based network for improved DEM interpolation and we further illustrate the GAN-based method’s performance compared to that of the CNN-based method.
Highlights
A digital elevation model (DEM) is a digital representation of terrain surfaces
The operating system used for the experiments was Windows 10, the central processing unit (CPU) was an Intel i9 7980XE, and the graphics processing unit (GPU) was a GeForce RTX 2080 Ti
Since gated convolution was computed on vectors including masks, the downsampled DEM images were concatenated with binary masks as the input, where the binary masks marked the locations to be interpolated
Summary
A digital elevation model (DEM) is a digital representation of terrain surfaces. DEMs can provide accurate geographic information and, play a crucial role in the related scientific research and practical applications such as mapping, hydrology, and meteorology.DEMs can be obtained from contour lines, topographic maps, field surveys, photogrammetry techniques, radar interferometry, and laser altimetry [1,2]. A digital elevation model (DEM) is a digital representation of terrain surfaces. DEMs can provide accurate geographic information and, play a crucial role in the related scientific research and practical applications such as mapping, hydrology, and meteorology. DEMs can be obtained from contour lines, topographic maps, field surveys, photogrammetry techniques, radar interferometry, and laser altimetry [1,2]. For all these techniques, interpolation is necessary to establish the values for all the terrain points. Different interpolation methods can result in different results, even when applied to the same source data. It is necessary to study the different interpolation methods and assess their suitability
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