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Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.

Accurate and complete digital elevation models (DEMs) play an important fundamental role in geospatial analysis, supporting various engineering applications, human activities, and scientific research. Interferometric synthetic aperture radar (InSAR) plays an increasingly important role in DEM generation. Nonetheless, owing to its inherent characteristics, gaps often appear in regions marked by significant topographical fluctuations, necessitating an extra void-filling process. Traditional void-filling methods have operated directly on preexisting data, succeeding in relatively flat terrain. When facing mountainous regions, there will always be gross errors in elevation values. Regrettably, conventional methods have often disregarded this vital consideration. To this end, this research proposes a DEM void-filling method based on incorporating elevation outlier detection. It accounts for the detection and removal of elevation outliers, thereby mitigating the shortcomings of existing methods and ensuring robust DEM restoration in mountainous terrains. Experiments were conducted to validate the method applicability using TanDEM-X data from Sichuan, China, Hebei, China, and Oregon, America. The results underscore the superiority of the proposed method. Three traditional methods are selected for comparison. The proposed method has different degrees of improvement in filling accuracy, depending on the void status of the local terrain. Compared with the delta surface fill (DSF) method, the root mean squared error (RMSE) of the filling results has improved by 7.87% to 51.87%. The qualitative and quantitative experiments demonstrate that the proposed method is promising for large-scale DEM void-filling tasks.

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face. [ABSTRACT FROM AUTHOR]

Tetrahedrites, due to their promising thermoelectric properties, are one of the materials being investigated for use in thermoelectric generators. One problem is the lack of n-type tetrahedrites, which would be beneficial for the design of tetrahedrite thermoelectric modules. Preliminary theoretical studies have shown that elements from groups I and II can be introduced into the structural voids of tetrahedrite, acting as donor dopants, and should enable n-type conductivity. Therefore, in this work, an attempt was made to obtain and study magnesium-doped tetrahedrites. A series of samples, MgxCu12Sb4S13, with different magnesium contents were obtained and their phase and chemical compositions were characterized. It was observed that the structural changes occurring upon doping indicate that Mg atoms are likely to be embedded in the structural voids. The experimental studies have been supported by electronic structure calculations indicating that the most likely location of Mg is in the structural voids at the 6b Wyckoff position. Seebeck coefficient and resistivity measurements showed that doping with Mg reduces the concentration of holes, which is consistent with the predicted donor character of the dopant. However, the introduction of magnesium in sufficient amounts to achieve n-type conductivity was not successful. [ABSTRACT FROM AUTHOR]

Generation of LiDAR-derived digital elevation model (DEM), particularly for hydrologic and shore environments, poses a continuous challenge. The presence of laser dropouts found on the water bodies causes data voids/holes in the airborne LiDAR data point clouds. Unnatural huge triangular artifacts may appear in these regions when a DEM is generated, resulting in not only unpleasant visual effect but also inaccurate terrain analyses. The United States Geological Survey has stressed the need of having a hydro-flattened DEM in the LiDAR Base Specification. Different forms of water bodies should be represented by a flat surface. Existing approaches mainly rely on the use of ancillary data or manual intervention during the hydroflattening process. In this study, an automatic data processing workflow is proposed to: 1) classify land and water data points collected by a topographic airborne LiDAR system based on the scan line intensity-elevation ratio; 2) perform scan line void filling of data points in close-to-nadir region and at both swath edges; 3) generate a virtual water surface based on the classified water data points; 4) perform hydroflattening on the DEM. The proposed workflow was examined using five datasets collected by topographic airborne LiDAR on the inland ponds and lakes, inland rivers, nontidal boundary water bodies, tidal water bodies, and islands, as addressed in the LiDAR Base Specification. The results showed that the proposed workflow can successfully generate hydroflattened DEMs and overcome the drawback of existing approaches.

Abstract Mining‐induced voids are the main circulation pathways for underground fluids such as water and coalbed methane. The collapse of these voids transmits to the ground surface, resulting in subsidence and building collapse. Accordingly, effective and feasible solutions are needed to control surface subsidence. In this study, a novel technique of void filling in the short‐wall caving zone was proposed to better control surface subsidence in thin coal seam mining. The width of the working face plays a key role in the proposed technique. A maximum surface subsidence value was predicted using numerical simulation and physical simulation experiments with different working face widths. The results indicate that the appropriate working face width should be less than 50 m for the studied coal mine. In this example, the surface subsidence coefficient was less than the standard value for initial damage to rural structures. Both advancing speed and productivity of the working face were achieved using the proposed technique because the filling and mining processes were conducted simultaneously on different faces. The results suggest that surface subsidence during thin coal seam mining could be controlled using the proposed technique. This technique can also help mitigate mining‐induced water inrush and gas leakage disasters by filling voids.

Tetrahedrites, due to their promising thermoelectric properties, are one of the materials being investigated for use in thermoelectric generators. One problem is the lack of n-type tetrahedrites, which would be beneficial for the design of tetrahedrite thermoelectric modules. Preliminary theoretical studies have shown that elements from groups I and II can be introduced into the structural voids of tetrahedrite, acting as donor dopants, and should enable n-type conductivity. Therefore, in this work, an attempt was made to obtain and study magnesium-doped tetrahedrites. A series of samples, MgxCu12Sb4S13, with different magnesium contents were obtained and their phase and chemical compositions were characterized. It was observed that the structural changes occurring upon doping indicate that Mg atoms are likely to be embedded in the structural voids. The experimental studies have been supported by electronic structure calculations indicating that the most likely location of Mg is in the structural voids at the 6b Wyckoff position. Seebeck coefficient and resistivity measurements showed that doping with Mg reduces the concentration of holes, which is consistent with the predicted donor character of the dopant. However, the introduction of magnesium in sufficient amounts to achieve n-type conductivity was not successful.

In this work we develop a void filling and void motion dynamics model using volatile pressure and squeeze flow during tape placement process. The void motion and filling are simulated using a non-local model where their presence is reflected in the global macroscale behavior. Local pressure gradients during compression do play a critical role in void dynamics, and hence the need for a non-local model. Deriving a non-local model accounting for all the void motion and dynamics entails a prohibitive number of degrees of freedom, leading to unrealistic computation times with classical solution techniques. Hence, Proper Generalized Decomposition – PGD – is used to solve the aforementioned model. In fact, PGD circumvents the curse of dimensionality by using separated representation of the space coordinates. For example, a 2D problem can be solved as a sequence of 1D problems to find the 2D solution. The non-local model solution sheds light on the fundamental of the void dynamics including their pressure variation, motion and closure mechanisms. Finally, a post treatment of the transient compression of the voids is used to derive conclusions regarding the physics of the void dynamics. [ABSTRACT FROM AUTHOR]

New default filters are proposed for datum cylinders subject to the ISO GPS family of standards. The filters are based on new geometric concepts: the intersection structured hull for outer cylinders and the union structured hull for inner cylinders. The hulls are mathematically defined, and their properties are illustrated graphically and analytically. The resulting filters' behavior is compared to the closing ball filter, showing improved effectiveness in representing part assembly in the presence of a variety of form errors, surface interruptions, and voids. [ABSTRACT FROM AUTHOR]

The investigation of form and processes in geomorphology and ecology is highly dependent on topographic data: a reliable digital terrain representation is in fact a key issue across environmental and earth sciences. In many cases, the processing of high‐resolution topographic data (e.g., light detection and ranging (LiDAR), structure from motion) has to face issues such as void filling, vegetation/feature removal and interpolation accuracy that are usually related to (i) intrinsic limitations of the adopted technology, (ii) local conditions affecting the survey or (iii) specific design scenario. In this paper, we develop a methodology to test the accuracy of an image inpainting algorithm to fill data voids in complex mountain areas. The devised experiment exploits the availability of a high‐resolution, LiDAR‐derived digital terrain model and the inpainting approach accuracy is checked against some widely used interpolation techniques (natural neighbor, spline, inverse distance weighting, kriging). In order to better mimic the actual surface texture, a methodology to introduce local topographic variability to the interpolated surface is also presented. The results show a better performance of the inpainting algorithm especially in the case of complex and rugged topography. Two examples showing an effective usage and accuracy of the proposed technique are reported, highlighting the drawbacks that a poor surface representation can introduce. The whole procedure is made freely available within a Matlab® script with the addition of sample files. ©2019 John Wiley & Sons, Ltd. ©2020 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Mining‐induced voids are the main circulation pathways for underground fluids such as water and coalbed methane. The collapse of these voids transmits to the ground surface, resulting in subsidence and building collapse. Accordingly, effective and feasible solutions are needed to control surface subsidence. In this study, a novel technique of void filling in the short‐wall caving zone was proposed to better control surface subsidence in thin coal seam mining. The width of the working face plays a key role in the proposed technique. A maximum surface subsidence value was predicted using numerical simulation and physical simulation experiments with different working face widths. The results indicate that the appropriate working face width should be less than 50 m for the studied coal mine. In this example, the surface subsidence coefficient was less than the standard value for initial damage to rural structures. Both advancing speed and productivity of the working face were achieved using the proposed technique because the filling and mining processes were conducted simultaneously on different faces. The results suggest that surface subsidence during thin coal seam mining could be controlled using the proposed technique. This technique can also help mitigate mining‐induced water inrush and gas leakage disasters by filling voids. [ABSTRACT FROM AUTHOR]

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.

The increasing availability of digital elevation models (DEMs) facilitates the monitoring of glacier mass balances on local and regional scales. Geodetic glacier mass balances are obtained by differentiating DEMs. However, these computations are usually affected by voids in the derived elevation change data sets. Different approaches, using spatial statistics or interpolation techniques, were developed to account for these voids in glacier mass balance estimations. In this study, we apply novel void filling techniques, which are typically used for the reconstruction and retouche of images and photos, for the first time on elevation change maps. We selected 6210 km2 of glacier area in southeast Alaska, USA, covered by two void-free DEMs as the study site to test different inpainting methods. Different artificially voided setups were generated using manually defined voids and a correlation mask based on stereoscopic processing of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) acquisition. Three “novel” (Telea, Navier–Stokes and shearlet) as well as three “classical” (bilinear interpolation, local and global hypsometric methods) void filling approaches for glacier elevation data sets were implemented and evaluated. The hypsometric approaches showed, in general, the worst performance, leading to high average and local offsets. Telea and Navier–Stokes void filling showed an overall stable and reasonable quality. The best results are obtained for shearlet and bilinear void filling, if certain criteria are met. Considering also computational costs and feasibility, we recommend using the bilinear void filling method in glacier volume change analyses. Moreover, we propose and validate a formula to estimate the uncertainties caused by void filling in glacier volume change computations. The formula is transferable to other study sites, where no ground truth data on the void areas exist, and leads to higher accuracy of the error estimates on void-filled areas. In the spirit of reproducible research, we publish a software repository with the implementation of the novel void filling algorithms and the code reproducing the statistical analysis of the data, along with the data sets themselves.

In the course of recent years, a lot of research has been directed in the ranges of optical packet switching and optical burst switching (OBS). This examination has been inspired by the requirement for procedures that are equipped for supporting the requesting prerequisites of developing dynamic high-transfer speed applications in an adaptable and effective way. In this paper we have undertaken the critical and mandatory factors of OBS such as (1) assembling the bursts, (2) routing (3) resource (such as transmission channels) scheduling and (4) burst segmenting for optimal transmission). Consequently the current research is focusing on these both of dissimilar variables of OBS and putting endeavors to contribute ideal ways to assemble, route, schedule or segment. This manuscript reviewed the nomenclature of the Optical Burst Switching Networks where fundamental ideas are examined and the aim is to identify the difficulties and strengths for optical packet and burst switching systems. This audit has been inspired by the requirement for methods that are equipped for supporting the future data transmission necessity of cutting edge applications and administrations of Internet Protocol systems. The authors have examined the fundamental ideas of OBS worldview and laid out issues identified with OBS systems. The future challenges that are still limiting the performance of the present contributions are told subsequently evincing the scope for future research. [ABSTRACT FROM AUTHOR]

Despite post-processing efforts by space agencies and research institutions, contemporary global digital elevation models (DEMs) may contain artefacts, i.e., erroneous features that do not exist in the actual terrain, such as spikes, holes and line errors. The goal of the present paper is to illuminate the artefact issue of current global DEM data sets that might be an obstacle for any geoscience study using terrain information. We introduce the Maximum Slope Approach (MSA) as a technique that uses terrain slopes as indicator to detect and localize spurious artefacts. The MSA relies on the strong sensitivity of terrain slopes for sudden steps in the DEM that is a direct feature of larger artefacts. In a numerical case study, the MSA is applied for globally complete screening of two SRTM-based 3 arc-second DEMs, the SRTM v4.1 and the MERIT-DEM. Based on 0.1° × 0.1° sub-divisions and a 5 m/m slope threshold, 1341 artefacts were detected in SRTM v4.1 vs. 108 in MERIT. Most artefacts spatially correlate with SRTM voids (and thus with the void-filling) and not with the SRTM-measured elevations. The strong contrast in artefact frequency (factor ~12) is attributed to the SRTM v4.1 hole filling. Our study shows that over parts of the Himalaya Mountains the SRTM v4.1 data set is contaminated by step artefacts where the use of this DEM cannot be recommended. Some caution should be exercised, e.g., over parts of the Andes and Rocky Mountains. The same holds true for derived global products that depend on SRTM v4.1, such as gravity maps. Primarily over the major mountain ranges, the MERIT model contains artefacts, too, but in smaller numbers. As a conclusion, globally complete artefact screening is recommended prior to the public release of any DEM data set. However, such a quality check should also be considered by users before using DEM data. MSA-based artefact screening is not only limited to DEMs, but can be applied as quality assurance measure to other gridded data sets such as digital bathymetric models or gridded physical quantities such as gravity or magnetics. [ABSTRACT FROM AUTHOR]

Void filling and anomaly replacement in conjunction with auxiliary sources of data have been widely used to improve the quality of existing problematic high-resolution digital elevation models. However, the traditional interpolation methods used for this purpose have always failed to eliminate the discrepancies between different data-sets. In this paper, an improved ANUDEM method is presented for DEM interpolation, which incorporates the idea of topographic correction using high correlation of topological structure between contour lines (CLs) from multi-scale digital elevation models (DEM). Firstly, the terrain topological structure is extracted from the CLs of a low-resolution DEM. The topographic surface correction is then undertaken based on the extracted structure, which recovers the topographic information of the sharp depressions and eminences to fit the high-resolution representation. Finally, the breaklines of the terrain surface are distilled and integrated into the denser DEM generation. The experiments undertaken confirmed the superiority of the proposed method over the other DEM interpolation methods. It is shown that the proposed method can provide results with a higher accuracy, as well as a better visual quality. [ABSTRACT FROM AUTHOR]

p-Type NdFeCoSb ( z = 0.8, 0.9, 1.0 and x = 0, 0.5, 1.0) skutterudites were synthesized by encapsulated melting and annealing, and consolidated with hot pressing. The effects of Nd filling and Co substitution for Fe (charge compensation) on the electronic transport and the thermoelectric properties of the skutterudites were examined. A few secondary phases such as Sb and FeSb were formed together with the skutterudite phase, but the formation was suppressed with increasing Nd and Co contents. It was confirmed that Nd was filled in the void and Co was substituted for Fe in all specimens, because the lattice constant increased with increasing Nd content and decreased with increasing Co content. The electrical conductivity of all specimens decreased slightly with increasing temperature, showing degenerate semiconductor characteristics. The Hall and the Seebeck coefficients showed positive signs, indicating that the major carriers were holes ( p-type conduction). The Seebeck coefficients were increased due to the decrease in the carrier concentration with increasing Nd and Co contents, while the electrical conductivity and the thermal conductivity were decreased. As a result, the dimensionless figure-of-merit, ZT, was improved by Nd filling and Co substitution, and a maximum ZT = 0.91 was obtained at 723 K for NdFeCoSb. [ABSTRACT FROM AUTHOR]

Digital Surface Models (DSM) derived from stereo-pair satellite images are the main sources for many Geo-Informatics applications like 3D change detection, object classification and recognition. However since occlusion especially in urban scenes result in some deficiencies in the stereo matching phase, these DSMs contain some voids. In order to fill the voids a range of algorithms have been proposed, mainly including interpolation alone or along with auxiliary DSM. In this paper an algorithm for void filling in DSM from stereo satellite images has been developed. Unlike common previous approaches we didn't use any external DSM to fill the voids. Our proposed algorithm uses only the original images and the unfilled DSM itself. First a neighborhood around every void in the unfilled DSM and its corresponding area in multispectral image is defined. Then it is analysed to extract both spectral and geometric texture and accordingly to assign labels to each cell in the voids. This step contains three phases comprising shadow detection, height thresholding and image segmentation. Thus every cell in void has a label and is filled by the median value of its co-labelled neighbors. The results for datasets from WorldView-2 and IKONOS are shown and discussed. [ABSTRACT FROM AUTHOR]

CAD based automatic geometry modeling tools have been widely applied to generate Monte Carlo (MC) calculation geometry for complex systems according to CAD models. Automatic void filling is one of the main functions in the CAD based MC geometry modeling tools, because the void space between parts in CAD models is traditionally not modeled while MC codes such as MCNP need all the problem space to be described. A dedicated void filling method, named Convex-based Void Filling (CVF), is proposed in this study for efficient void filling and concise void descriptions. The method subdivides all the problem space into disjointed regions using Quality based Subdivision (QS) and describes the void space in each region with complementary descriptions of the convex volumes intersecting with that region. It has been implemented in SuperMC/MCAM, the Multiple-Physics Coupling Analysis Modeling Program, and tested on International Thermonuclear Experimental Reactor (ITER) Alite model. The results showed that the new method reduced both automatic modeling time and MC calculation time. [ABSTRACT FROM AUTHOR]

The method for high accuracy surface modeling (HASM), inverse distance weighting (IDW) and ordinary Kriging (OK) are used to fill voids on XCO 2 surfaces of GOSAT and SCIAMACHY. Inner voids and boundary voids are artificially made by cleaning out the downloaded data, where there are no voids, in different latitude belts of the northern hemisphere and the southern hemisphere at random. The cleaned-out data in the artificial voids are selected as the ‘true’ values for verification. The results demonstrated that HASM always has the highest accuracy compared with the classical methods of IDW and OK, whether voids are inner ones or boundary ones, data sets are from GOSAT or from SCIAMACHY, and the void areas are larger or smaller. HASM is an alternative approach to filling voids on XCO 2 surfaces from the satellites. [ABSTRACT FROM AUTHOR]

Several dynamic bandwidth allocation (DBA) algorithms have been proposed for long-reach passive optical networks (LR-PONs). The gains in the current protocols, however, are incremental and limited to a specific range of loads. Using an analytical framework, we first examine the main sources of performance degradation specific to LR-PONs. From this, we conclude that there is a large scope for improvement in the performance by using an optimal cycle length, which is not influenced by reach, and which polls users according to their reported load while providing opportunities for pre-granting bandwidth at the optical network units. The fraction of bandwidth that is pre-granted should be decreased with load. To tailor these optimal design concepts in a DBA algorithm, we propose Synergized-Adaptive Multi-GATE polling with Void-filling (S-AMGAV), which reduces both the delay bound and packet loss rate by at least 50% when compared to the state-of-the-art DBAs like interleaved polling with adaptive cycle time (IPACT), IPACT with grant estimation (IPACT-GE), multi-thread polling (MTP), double phase polling (DPP), gate driven (GD), and AMGAV. S-AMGAV also maintains simplicity of the protocol while removing bandwidth inefficiencies prevailing in the existing protocols. We verify the usefulness of the algorithm through both analytical modeling and simulations. [ABSTRACT FROM PUBLISHER]

This work investigates void filling (idle periods) in long-reach Ethernet passive optical networks. We focus on reducing grant delays and hence reducing the average packet delay. We introduce a novel approach called parallel void thread (PVT), which allocates bandwidth grants during voids baseless of bandwidth requests. We introduce three different grant sizing schemes for PVT, namely void extension, count controlled batch void filling, and size controlled batch void filling. The proposed approaches canbe integrated with almost all of the previously reported dynamic bandwidth allocation schemes. Unlike other void filling schemes, PVT is less sensitive to the differential distance between optical network units and can work very well in the case of limited differential distances. We have analytically investigated the packet delay and derived a bound condition for PVT to outperform the other competitors. We support our work with extensive simulation study considering bursty traffic with long range dependence for both the single-class and differentiated services (DiffServ) scenarios. Numerical results show delay reduction up to 35% compared with the non-void filling scheme for the single-class scenario. For DiffServ traffic, PVT achieves delay reduction up to 80% for expedited forward traffic, 52% for assured forward traffic, and 56% for best effort traffic. [ABSTRACT FROM PUBLISHER]

McCad is a geometry conversion tool developed at KIT to enable the automatic bi-directional conversions of CAD models into the Monte Carlo (MC) geometries utilized for neutronics calculations (CAD to MC) and, reversed (MC to CAD), for visualization purposes. The paper presents the latest improvements of the conversion algorithms including improved decomposition, void filling and an advanced interface for the materials editing and assignment. The new implementations and features were tested on fusion neutronics applications to the DEMO and ITER NBI (Neutral Beam Injector) models. The results demonstrate greater stability and enhanced efficiency of McCad conversion process. [ABSTRACT FROM AUTHOR]

The Digital Elevation Model that has been derived from the February 2000 Shuttle Radar Topography Mission (SRTM) has been one of the most important publicly available new spatial data sets in recent years. However, the 'finished' grade version of the data (also referred to as Version 2) still contains data voids (some 836,000 km2) - and other anomalies - that prevent immediate use in many applications. These voids can be filled using a range of interpolation algorithms in conjunction with other sources of elevation data, but there is little guidance on the most appropriate void-filling method. This paper describes: (i) a method to fill voids using a variety of interpolators, (ii) a method to determine the most appropriate void-filling algorithms using a classification of the voids based on their size and a typology of their surrounding terrain; and (iii) the classification of the most appropriate algorithm for each of the 3,339,913 voids in the SRTM data. Based on a sample of 1304 artificial but realistic voids across six terrain types and eight void size classes, we found that the choice of void-filling algorithm is dependent on both the size and terrain type of the void. Contrary to some previous findings, the best methods can be generalised as: kriging or inverse distance weighting interpolation for small and medium size voids in relatively flat low-lying areas; spline interpolation for small and medium-sized voids in high-altitude and dissected terrain; triangular irregular network or inverse distance weighting interpolation for large voids in very flat areas, and an advanced spline method (ANUDEM) for large voids in other terrains. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEMs) are an important information source for spatial modeling. However, data voids, which commonly exist in regions with rugged topography, result in incomplete DEM products, and thus significantly degrade DEM data quality. Interpolation methods are commonly used to fill voids of small sizes. For large-scale voids, multi-source fusion is an effective solution. Nevertheless, high-quality auxiliary source information is always difficult to retrieve in rugged mountainous areas. Thus, the void filling task is still a challenge. In this paper, we proposed a method based on a deep convolutional generative adversarial network (DCGAN) to address the problem of DEM void filling. A terrain texture generation model (TTGM) was constructed based on the DCGAN framework. Elevation, terrain slope, and relief degree composed the samples in the training set to better depict the terrain textural features of the DEM data. Moreover, the resize-convolution was utilized to replace the traditional deconvolution process to overcome the staircase in the generated data. The TTGM was trained on non-void SRTM (Shuttle Radar Topography Mission) 1-arc-second data patches in mountainous regions collected across the globe. Then, information neighboring the voids was involved in order to infer the latent encoding for the missing areas approximated to the distribution of training data. This was implemented with a loss function composed of pixel-wise, contextual, and perceptual constraints during the reconstruction process. The most appropriate fill surface generated by the TTGM was then employed to fill the voids, and Poisson blending was performed as a postprocessing step. Two models with different input sizes (64 × 64 and 128 × 128 pixels) were trained, so the proposed method can efficiently adapt to different sizes of voids. The experimental results indicate that the proposed method can obtain results with good visual perception and reconstruction accuracy, and is superior to classical interpolation methods. [ABSTRACT FROM AUTHOR]