scholarly journals DEM Void Filling Based on Context Attention Generation Model

2020 ◽  
Vol 9 (12) ◽  
pp. 734
Author(s):  
Chunsen Zhang ◽  
Shu Shi ◽  
Yingwei Ge ◽  
Hengheng Liu ◽  
Weihong Cui
Keyword(s):  
Interpolation Method ◽  
Digital Simulation ◽  
Generation Model ◽  
Generative Adversarial Network ◽  
Cloud Data ◽  
Adversarial Network ◽  
Network Training ◽  
Digital Elevation ◽  
Elevation Model ◽  
Feature Expression

The digital elevation model (DEM) generates a digital simulation of ground terrain in a certain range with the usage of 3D point cloud data. It is an important source of spatial modeling information. Due to various reasons, however, the generated DEM has data holes. Based on the algorithm of deep learning, this paper aims to train a deep generation model (DGM) to complete the DEM void filling task. A certain amount of DEM data and a randomly generated mask are taken as network inputs, along which the reconstruction loss and generative adversarial network (GAN) loss are used to assist network training, so as to perceive the overall known elevation information, in combination with the contextual attention layer, and generate data with reliability to fill the void areas. The experimental results have managed to show that this method has good feature expression and reconstruction accuracy in DEM void filling, which has been proven to be better than that illustrated by the traditional interpolation method.

scholarly journals High Accuracy Interpolation of DEM Using Generative Adversarial Network

2021 ◽  
Vol 13 (4) ◽  
pp. 676
Author(s):  
Li Yan ◽  
Xingfen Tang ◽  
Yi Zhang
Keyword(s):  
Generative Adversarial Networks ◽  
Traditional Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Interpolation Accuracy ◽  
Quantitative Accuracy ◽  
Digital Elevation ◽  
Convolution Structure ◽  
Elevation Model

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.

scholarly journals Rockfall Risk Analysis Based on Precise Topography using UAV

2020 ◽  
Vol 20 (4) ◽  
pp. 63-73
Author(s):  
Jaehee Choi ◽  
Namgyun Kim ◽  
Bongjin Choe ◽  
Byonghee Jun
Keyword(s):  
Point Cloud Data ◽  
University Campus ◽  
Digital Map ◽  
Cloud Data ◽  
The Road ◽  
Mountainous Regions ◽  
Digital Elevation ◽  
Surface Models ◽  
Aerial Vehicle ◽  
Elevation Model

In this study, the risk of rockfall on incision slopes adjacent to roads was evaluated using the RocFall program. The study area was a slope adjacent to the road leading to a university campus in Samcheok-si, Gangwon-do, with an area of 774 m<sup>2</sup> and an average slope of approximately 43°. A rock shed was installed at the lower zone of the slope. A 3D model of the terrain was generated based on point cloud data gathered using a UAV (unmanned aerial vehicle). Fast and accurate orthoimages were captured by UAV and high-resolution digital surface models (DSMs) were produced; these data were used to assess the risk of rockfall. Compared to terrain extraction using a digital elevation model (DEM) generated from an existing digital map, terrain extraction using a UAV was more effective in deriving results close to the actual situation in the field, especially for the analysis of rockfall jump height and kinetic energy. The necessity of constructing 3D topographic data using UAVs to predict rockfall disasters in mountainous regions was confirmed.

scholarly journals Effect of DEM Interpolation Neighbourhood on Terrain Factors

2019 ◽  
Vol 8 (1) ◽  
pp. 30 ◽  
Author(s):  
Ying Zhu ◽  
Xuejun Liu ◽  
Jing Zhao ◽  
Jianjun Cao ◽  
Xiaolei Wang ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Simulated Data ◽  
Topographic Features ◽  
Structure And Morphology ◽  
Digital Elevation ◽  
Different Types ◽  
Slope Error ◽  
Elevation Model ◽  
The Impact ◽  
Mls Method

Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical.

scholarly journals EVALUATION DIGITAL ELEVATION MODEL GENERATED BY SYNTHETIC APERTURE RADAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 57-62
Author(s):  
H. B. Makineci ◽  
H. Karabörk
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Interpolation Method ◽  
Digital Elevation Models ◽  
European Space Agency ◽  
Mountain Area ◽  
Synthetic Aperture Radar Data ◽  
Radar Images ◽  
Digital Elevation ◽  
Elevation Model

Digital elevation model, showing the physical and topographical situation of the earth, is defined a tree-dimensional digital model obtained from the elevation of the surface by using of selected an appropriate interpolation method. DEMs are used in many areas such as management of natural resources, engineering and infrastructure projects, disaster and risk analysis, archaeology, security, aviation, forestry, energy, topographic mapping, landslide and flood analysis, Geographic Information Systems (GIS). Digital elevation models, which are the fundamental components of cartography, is calculated by many methods. Digital elevation models can be obtained terrestrial methods or data obtained by digitization of maps by processing the digital platform in general. Today, Digital elevation model data is generated by the processing of stereo optical satellite images, radar images (radargrammetry, interferometry) and lidar data using remote sensing and photogrammetric techniques with the help of improving technology. <br><br> One of the fundamental components of remote sensing radar technology is very advanced nowadays. In response to this progress it began to be used more frequently in various fields. Determining the shape of topography and creating digital elevation model comes the beginning topics of these areas. <br><br> It is aimed in this work , the differences of evaluation of quality between Sentinel-1A SAR image ,which is sent by European Space Agency ESA and Interferometry Wide Swath imaging mode and C band type , and DTED-2 (Digital Terrain Elevation Data) and application between them. The application includes RMS static method for detecting precision of data. Results show us to variance of points make a high decrease from mountain area to plane area.

scholarly journals ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

2016 ◽  
Vol XLI-B5 ◽  
pp. 69-76 ◽  
Author(s):  
Lindsay MacDonald ◽  
Isabella Toschi ◽  
Erica Nocerino ◽  
Mona Hess ◽  
Fabio Remondino ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
High Frequency ◽  
Point Cloud ◽  
Laser Scanner ◽  
Low Frequency ◽  
Photometric Stereo ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Digital Elevation ◽  
Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

scholarly journals ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

2016 ◽  
Vol XLI-B5 ◽  
pp. 69-76
Author(s):  
Lindsay MacDonald ◽  
Isabella Toschi ◽  
Erica Nocerino ◽  
Mona Hess ◽  
Fabio Remondino ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
High Frequency ◽  
Point Cloud ◽  
Laser Scanner ◽  
Low Frequency ◽  
Photometric Stereo ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Digital Elevation ◽  
Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

scholarly journals Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Kai Liang ◽  
Haijun Zhao
Keyword(s):  
Sound Production ◽  
Production Model ◽  
Audio Signals ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Moment Estimation ◽  
Network Training ◽  
Automobile Engines ◽  
Human Ear ◽  
Leave One Out

To improve the diversity and quality of sound mimicry of electric automobile engines, a generative adversarial network (GAN) model was used to construct an active sound production model for electric automobiles. The structure of each layer in the network in this model and the size of its convolution kernel were designed. The gradient descent in network training was optimized using the adaptive moment estimation (Adam) algorithm. To demonstrate the quality difference of the generated samples from different input signals, two GAN models with different inputs were constructed. The experimental results indicate that the model can accurately learn the characteristic distributions of raw audio signals. Results from a human ear auditory test show that the generated audio samples mimicked the real samples well, and a leave-one-out (LOO) test show that the diversity of the samples generated from the raw audio signals was higher than that of samples generated from a two-dimensional spectrogram.

scholarly journals Solar Radiation Estimation Using Data Mining Techniques for Remote Areas—A Case Study in Ethiopia

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5714
Author(s):  
Bizuayehu Abebe Worke ◽  
Hans Bludszuweit ◽  
José A. Domínguez-Navarro
Keyword(s):  
Data Mining ◽  
Solar Radiation ◽  
Digital Elevation Model ◽  
Interpolation Method ◽  
Inference System ◽  
Remote Areas ◽  
Radiation Data ◽  
Digital Elevation ◽  
Elevation Model

High quality of solar radiation data is essential for solar resource assessment. For remote areas this is a challenge, as often only satellite data with low spatial resolution are available. This paper presents an interpolation method based on topographic data in digital elevation model format to improve the resolution of solar radiation maps. The refinement is performed with a data mining method based on first-order Sugeno type Adaptive Neuro-Fuzzy Inference System. The training set contains topographic characteristics such as terrain aspect, slope and elevation which may influence the solar radiation distribution. An efficient sampling method is proposed to obtain representative training sets from digital elevation model data. The proposed geographic information system based approach makes this method reproducible and adaptable for any region. A case study is presented on the remote Amhara region in North Shewa, Ethiopia. Results are shown for interpolation of solar radiation data from 10 km × 10 km to a resolution of 1 km × 1 km and are validated with data from the PVGIS and SWERA projects.

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