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Numerically interpolated contour lines (NICL) developed from a high-resolution, accurate digital elevation model (DEM) can be considered the most accurate contour baseline available from surveyed data. Displays of this baseline, however, can exhibit an undesirable angularity and clutter that impedes human understanding of the data and defies the universal appeal of a smooth curve. To achieve smooth, readable lines, the prevailing wisdom today is to either 1) smooth the underlying surface model before creating the lines or 2) use mathematical methods adapted from computer-aided design (CAD) to smooth the lines in x and y. However, these smoothing methods can result in an inaccurate rendition of the original data that undermines their subsequent use in analysis and modeling applications. Upon close inspection, lines generated from such methods can depart from the baseline by more than a definable and acceptable distance in x and y, particularly noticeable in important areas of slope change. Here, we argue that generalized contours have a geometric accuracy requirement that must be observed. We present a novel alternative to the prevailing methods that 1) presents an incremental smoothing method that is a logical complement to Douglas-Poiker thinning; 2) introduces the use of slope as a means of controlling the degree of smoothing locally; 3) produces novel, operational definitions of tests for smoothness and accuracy as well as for line symmetry; and 4) introduces the notion of a topological TIN as a new type of terrain model and a medium for edits of contours. The method only manipulates the baseline contours in x and y. By using only simple proportions of sides and self-similar geometry to smooth the original lines, we at the same time impose computable, dynamic constraints on the smoothing. This combination ensures the accuracy and acceptability of the final contours at scales appropriate for working GIS displays. The result is a Locally Adjusted Curve Approximation (LACA) technique for contour line smoothing.

Existing algorithms for terrain extraction from LiDAR require data to be structured in either a regular grid or triangulated irregular network (TIN) format, and they involve two-step processes; namely filtering and interpolation. This paper introduces a new terrain model based on reversing common interpolation techniques. A new method is presented to extract terrain directly through the inverse interpolation model in a single step. In addition, no raster or TIN format is required to represent the LiDAR data. Instead, points can be processed sequentially as obtained by the laser scanner. Furthermore, additional constraints for handling missing data, which occurs within waterbodies or occluded areas, are introduced in this paper. Several examples of extracted terrain in a variety of scenarios are presented and compared to results from existing algorithms both numerically and visually by inspecting vertical profiles across the data. The new terrain model was found to be in very close proximity to that obtained using existing methods but the former provided a smoother version of the terrain. [ABSTRACT FROM AUTHOR]

Obtaining comprehensive and accurate terrain data is important for engineering construction. Unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are two widely used terrain modeling techniques. In mountainous areas, both techniques suffer limitations. These limitations occur in uninhabited areas, primarily caused by the steep terrain and inconvenient transportation conditions, resulting in poor data integrity and inadequate accuracy in UAV and TLS terrain mapping. In this article, we proposed a fusion modeling method based on UAV photogrammetry and TLS for high-precision terrain mapping in mountainous and canyon areas. The proposed method entails the use of TLS data to provide additional control points for UAV modeling, resulting in an improved accuracy of the modeling results. In addition, to quantify the optimization effect of this method, we proposed a 3-D model deviation comparison method based on the iterative closest point algorithm. This method can be employed to accurately depict the differences in distance and rotation angle between multiple terrain models. We applied this method to the Yebatan hydropower station in Southwest China, which increased the accuracy of the terrain data by 26% and expanded the effective range by over 100%.

Numerically interpolated contour lines (NICL) developed from a high-resolution, accurate digital elevation model (DEM) can be considered the most accurate contour baseline available from surveyed data. Displays of this baseline, however, can exhibit an undesirable angularity and clutter that impedes human understanding of the data and defies the universal appeal of a smooth curve. To achieve smooth, readable lines, the prevailing wisdom today is to either 1) smooth the underlying surface model before creating the lines or 2) use mathematical methods adapted from computer-aided design (CAD) to smooth the lines in x and y. However, these smoothing methods can result in an inaccurate rendition of the original data that undermines their subsequent use in analysis and modeling applications. Upon close inspection, lines generated from such methods can depart from the baseline by more than a definable and acceptable distance in x and y, particularly noticeable in important areas of slope change. Here, we argue that generalized contours have a geometric accuracy requirement that must be observed. We present a novel alternative to the prevailing methods that 1) presents an incremental smoothing method that is a logical complement to Douglas-Poiker thinning; 2) introduces the use of slope as a means of controlling the degree of smoothing locally; 3) produces novel, operational definitions of tests for smoothness and accuracy as well as for line symmetry; and 4) introduces the notion of a topological TIN as a new type of terrain model and a medium for edits of contours. The method only manipulates the baseline contours in x and y. By using only simple proportions of sides and self-similar geometry to smooth the original lines, we at the same time impose computable, dynamic constraints on the smoothing. This combination ensures the accuracy and acceptability of the final contours at scales appropriate for working GIS displays. The result is a Locally Adjusted Curve Approximation (LACA) technique for contour line smoothing. [ABSTRACT FROM AUTHOR]

UAV-SfM photogrammetry is widely used in remote sensing and geoscience communities. Scholars have tried to optimize UAV-SfM for terrain modeling based on analysis of error statistics like root mean squared error (RMSE), mean error (ME), and standard deviation (STD). However, the errors of terrain modeling tend to be spatially distributed. Although the error statistic can represent the magnitude of errors, revealing spatial structures of errors is still challenging. The "best practice" of UAV-SfM is lacking in research communities from the perspective of spatial structure of errors. Thus, this study designed various UAV-SfM photogrammetric scenarios and investigated the effects of image collection strategies and GCPs on terrain modeling. The error maps of different photogrammetric scenarios were calculated and quantitatively analyzed by ME, STD, and Moran's I. The results show that: (1) A high camera inclination (20–40°) enhances UAV-SfM photogrammetry. This not only decreases the magnitude of errors, but also mitigates its spatial correlation (Moran's I). Supplementing convergent images is valuable for reducing errors in a nadir camera block, but it is unnecessary when the image block is with a high camera angle. (2) Flying height increases the magnitude of errors (ME and STD) but does not affect the spatial structure (Moran's I). By contrast, the camera angle is more important than the flying height for improving the spatial structure of errors. (3) A small number of GCPs rapidly reduce the magnitude of errors (ME and STD), and a further increase in GCPs has a marginal effect. However, the structure of errors (Moran's I) can be further improved with increasing GCPs. (4) With the same number, the distribution of GCPs is critical for UAV-SfM photogrammetry. The edge distribution should be first considered, followed by the even distribution. The research findings contribute to understanding how different image collection scenarios and GCPs can influence subsequent terrain modeling accuracy, precision, and spatial structure of errors. The latter (spatial structure of errors) should be routinely assessed in evaluations of the quality of UAV-SfM photogrammetry. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Bauxite, in central Guizhou, is predominantly karst bauxite, but there is insufficient research on the effect of karst paleogeomorphology on bauxite development. Xiaoyuan bauxite is also a karst bauxite, and high- and low-iron bauxite deposits exist in the study area. This study conducts geological modeling of karst bauxite using controlled-source audio-frequency magnetotelluric (CSAMT) data and drill core data. The effects of karst paleogeomorphology on bauxite deposition and mineralization are evaluated by assessing karst paleogeomorphology, conducting a mineralogical analysis of drill cores at different locations, and determining the geochemical distribution characteristics of the elements in the horizontal and vertical directions. Combined with previous research results, we propose two metallogenic processes of high-iron and low-iron bauxite. The findings are significant for understanding the mechanism of bauxite formation. [ABSTRACT FROM AUTHOR]

We present a novel proposal for modeling complex dynamic terrains that offers real-time rendering, dynamic updates and physical interaction of entities simultaneously. We can capture any feature from landscapes including tunnels, overhangs and caves, and we can conduct a total destruction of the terrain. Our approach is based on a Constructive Solid Geometry tree, where a set of spheres are subtracted from a base Digital Elevation Model. Erosions on terrain are easily and efficiently carried out with a spherical sculpting tool with pixel-perfect accuracy. Real-time rendering performance is achieved by applying a one-direction CPU–GPU communication strategy and using the standard depth and stencil buffer functionalities provided by any graphics processor. • Real-time rendering using the standard depth and stencil buffers. • Real-time dynamic updates and physical interaction on complex dynamic terrains. • Erosions with a spherical sculpting tool with pixel-perfect accuracy. • The model easily captures tunnels, overhangs and terrain erosion. [ABSTRACT FROM AUTHOR]

The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43–2.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry. [ABSTRACT FROM AUTHOR]

We propose a family of spatial data structures for the representation and processing of Triangulated Irregular Networks (TINs). We call such data structures Terrain trees. A Terrain tree combines a minimal encoding of the connectivity of the TIN with a hierarchical spatial index. Connectivity relations are extracted locally at run-time, within each leaf block of the hierarchy, based on specific application needs. Spatial queries are performed by exploring the hierarchical data structure. We present a new framework for terrain analysis based on Terrain trees. The framework, implemented in the Terrain trees library (TTL), contains algorithms for morphological features extraction, such as roughness and curvature, and for topology-based analysis of terrains. Moreover, it includes a technique for multivariate visualization, which enables the analysis of multiple scalar fields defined on the same terrain. To prove the effectiveness and scalability of such framework, we have compared the different Terrain trees against each other and also against the most compact state-of-the-art data structure for TINs. Comparisons are performed on storage and generation costs and on the efficiency in performing terrain analysis operations. [ABSTRACT FROM AUTHOR]

High Mountain Asia (HMA) is characterized by some of the most complex and rugged terrain conditions in the world. However, high resolution terrain data are not easy to quickly acquire from the area due to difficulties in accessing the region. In this study, we trained a modified super-resolution residual network (MSRResNet) to develop super-resolution (SR) digital elevation models (DEMs) in the HMA areas by using freely available DEM data from the HMA region and limited high resolution (HR) DEMs from other areas to train the model. In this network, a new loss function was constructed that considered the terrain parameters of slope and curvature to constrain the network learning and convergence. The proposed method was applied to and validated by data from the Hengduan Mountains in the southeastern part of HMA, which is a world-famous longitudinal belt of mountains and canyons. A comparative analysis between the current and existing methods (i.e., SRGAN and Bicubic interpolation) was conducted to assess the effectiveness of the proposed approach. The experimental results were also investigated and evaluated by visual inspection and analysis of the terrain parameters. The results demonstrate that the proposed MSRResNet super-resolution process can achieve highly accurate terrain data by downscaling DEMs in HMA. This SR process also outperforms the other comparable methods. Compared to the Bicubic interpolation method, the RMSE and MAE accuracy are improved by 32.17% and 33.97%, and compared to the SRGAN method, the RMSE and MAE accuracy are improved by 39.15% and 32.47%. The HR DEM generated by the new method is more conducive to improving the accuracy of extracted terrain features, such as stream networks. It is promising to apply this model on other areas on Earth or even other planets with terrain similar to that of HMA.

The vast amount of data captured by robots in large‐scale unstructured environments brings computation and storage problems to terrain modeling methods. Considering the practical terrain geometry structures, we design a large‐scale terrain modeling framework for efficiently generating the compact and accurate terrain models from incomplete and uncertain measurements. The multiresolution elevation filtering is developed to generate terrain surfaces that preserve terrain details in the bumpy areas while achieving a sparse representation over the flat areas, which simultaneously reduces the computation time, storage space, and inference error. To further reduce the high computational complexity, we propose a computation decomposition strategy by adopting the Gaussian mixture model to partition the whole terrain surface into several subsurfaces, enabling the method to scale to much larger environments. For each subsurface, we train a Gaussian process model using the local data to adapt to the local terrain structure, concurrently resolving the data incompleteness and uncertainty. Furthermore, to provide a more informative terrain model, we efficiently derive terrain gradient through a closed‐form solution by treating the generated terrain models as continuous functions, enabling more robotics applications, such as the path planning. We perform extensive experiments to evaluate our framework, including the use of two medium‐sized terrain data sets to evaluate the overall and local performance, and two large‐scale glacier terrain and mountain terrain in the area of up to 9 km2 to validate the practical applications. [ABSTRACT FROM AUTHOR]

The development of digital terrain models in civil engineering for the purpose of modern, efficient and economical construction has become an indispensable part of geodetic work in modern countries such as Germany. By using well – known machine navigation technologies, it is possible to transfer a large part of the execution plan into reality, i.e. into the field. The method of obtaining models from execution plans is a complex process that requires a good knowledge of how to make the upper and lower layer of the road or railway. The paper presents a method of making a 3D model by developing cross - sections, and preparing a model for field transfer and errors and complications that may occur during this process.

Remotely piloted aerial systems (RPASs) are gaining fast and wide application around the world due to its relative low-cost advantage in the acquisition of high-resolution imagery. However, standardized protocols for the construction of cartographic products are needed. The aim of this paper is to optimize the generation of digital terrain models (DTMs) by using different RPAS flight parameters. An orthogonal design L18 was used to measure the effect of photogrammetric flight parameters on the DTM generated. The image data were acquired using a DJI Phantom 4 Pro drone and six flight parameters were evaluated: flight mode, altitude, flight speed, camera tilt, longitudinal overlap and transversal overlap. Fifty-one ground control points were established using a global positioning system. Multivision algorithms were used to obtain ultra-high resolution point clouds, orthophotos and 3D models from the photos acquired. Root mean square error was used to measure the geometric accuracy of DTMs generated. The effect of photogrammetric flight parameters was carried out by using analysis of variance statistical analysis. Altimetric and planimetric accuracies of 0.38 and 0.11 m were achieved, respectively. Based on these results, high-precision cartographic material was generated using low-cost technology.

The development of digital terrain models in civil engineering for the purpose of modern, efficient and economical construction has become an indispensable part of geodetic work in modern countries such as Germany. By using well – known machine navigation technologies, it is possible to transfer a large part of the execution plan into reality, i.e. into the field. The method of obtaining models from execution plans is a complex process that requires a good knowledge of how to make the upper and lower layer of the road or railway. The paper presents a method of making a 3D model by developing cross - sections, and preparing a model for field transfer and errors and complications that may occur during this process. [ABSTRACT FROM AUTHOR]

We propose an original method for generating planets with a high level of detail in real time. Our approach relies on a procedural hyper-amplification algorithm: a controlled subdivision process that faithfully reproduces landforms and hydrosphere features at different scales. Starting from low-resolution user-defined control maps providing information about the elevation, the presence of large-scale water bodies and landforms types, we apply subdivision rules to obtain a high-resolution hydrologically consistent planet model. We first generate large-scale river networks connected to inner seas and oceans and then synthesize the detailed hydrographic landscapes, including river tributaries and lakes, mountain ranges, valleys, plateaus, deserts and hills systems. Our GPU implementation allows to interactively explore planets that are produced by tectonic simulations, generated procedurally or authored by artists. [ABSTRACT FROM AUTHOR]

Copyright of Acta Geoscientica Sinica is the property of Acta Geoscientica Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We show a fractal surface generation method that, unlike other methods, generates both random and deterministic fractals that model natural and architectural elements. The method starts with a succession of sets of sites, which determine, by means of a metric, a succession of Voronoi tessellations of the region where the fractal is defined. For each element of the tessellation sequence we define a tessellation function which depends on each tile. This generates a succession of tessellation functions that will be the parameter of the same seed function. Finally, the fractal is generated by a weighted sum of the seed function evaluated on each value of the succession of parameters. If the sites used to generate the Voronoi tessellation are random, natural elements such as mountains, craters, lakes, etc. are generated; if they are deterministic, architectural and decorative elements are generated. In addition, the designers can control the morphology of the generated fractal by simply varying the metric. • Procedural method, easy to implement, which gives designers a wide range of control. • Based on the sum of functions over a succession of Voronoi tessellation. • The Voronoi tessellation can be random or deterministic. • The method generates random and deterministic fractal surfaces indistinctly. [ABSTRACT FROM AUTHOR]

Trajectory optimization precisely scanning an irregular terrain is a challenging problem since the trajectory optimizer needs to handle complex geometry topology, vehicle performance, and a sensor specification. To address these problems, this paper introduces a novel framework of a multi-UAV trajectory optimization method for an aerial imaging mission in an irregular terrain environment. The proposed framework consists of terrain modeling and multi-UAV trajectory optimization. The terrain modeling process employs a Non-Uniform Rational B-Spline (NURBS) surface fitting method based on point cloud information resulting from an airborne LiDAR sensor or other sensor systems. The NURBS-based surface model represents a computationally efficient terrain topology. In the trajectory optimization method, the framework introduces a multi-UAV vehicle routing problem enabling UAV to scan an entire area of interest, and obtains feasible trajectories based on given vehicle performance characteristics, and sensor specifications, and the approximated terrain model. The proposed multi-UAV trajectory optimization algorithm is tested by representative numerical simulations in a realistic aerial imaging environment, namely, San Diego and Death Valley, California. [ABSTRACT FROM AUTHOR]

The ability to generate accurate terrain models is of key importance in a wide variety of robotics tasks, ranging from path planning and trajectory optimization to environment exploration and mining applications. This paper introduces a novel regression methodology for terrain modeling that can approximate arbitrarily complex functions based on a series of simple kernel calculations, using variational Bayesian inference. A sparse feature vector is used to efficiently project input points into a high-dimensional reproducing kernel Hilbert space, according to a set of inducing points automatically generated from clustering available data. Each inducing point maintains its own regression model in addition to individual kernel parameters, and the entire set is iteratively optimized as more data are collected in order to maximize a global variational lower bound. We also show how kernel and regression model parameters can be jointly learned, to achieve a better approximation of the underlying function. Experimental results show that the proposed methodology consistently outperforms current state-of-the-art techniques, while producing a continuous model with a fully probabilistic treatment of uncertainties, well-defined gradients, and highly scalable to large-scale datasets. As a practical application of the proposed terrain modeling technique, we explore the problem of trajectory optimization, deriving gradients that allow the efficient generation of continuous paths using standard optimization algorithms, minimizing a series of useful properties (i.e. distance traveled, changes in elevation, and terrain variance). [ABSTRACT FROM AUTHOR]

When comparing topographic models generated through conventional methodology, which uses total station, with the models generated through UAV flight, aerial photogrammetry allows a larger volume of data that, through appropriate adjustment, provides reliable information of high precision, in addition to higher quality in the presentation of the product. The aim of this work is to compare the planimetric models produced with the purpose of verifying the progress of exploitation in multiple granite benches through the two methodologies mentioned. Hence, a survey was performed using the total station followed by the production of a model. Simultaneously, flights with different parameters were executed in order to select the Digital Surface Model (DSM) with the lowest global error, followed by the construction of another model, validated through the altimetric comparison in several points of this with the height measured in the field. Finally, it was possible to compare the two products from the theoretical width of the slopes and the one presented by the models between the foot and the projection of the crest of several points of the slopes, allowing concluding that the model generated using UAV presents greater coherence and reliability. [ABSTRACT FROM AUTHOR]

Landscape is considered a specific type of heritage, and cultural landscapes provide an interface between nature and culture, the tangible and intangible, and biological and cultural diversity. They represent a closely woven network of relationships and the essence of cultural and personal identity. The most valuable cultural landscapes are designed landscapes or landscape parks. This paper focuses on landscaping activities associated with park foundation and management, especially those involving terrain changes and the remodeling of the natural topography. Terrain changes in landscape parks are typically meant to be hidden from viewers and to mimic natural lines and shapes. The paper focuses on determining to what degree the natural topography was used and changed, as well as what impact it had on the form and creation of the parks. Terrain changes should differ according to the natural topography, the landscape design activities, and contemporary landscape trends. Archival sources, including written documents, maps, and pictures, were considered viable sources. Four model areas were chosen for a detailed analysis of landscaping and design activities and their impact on the terrain: landscape parks around manor houses in Krásný Dvůr, Jemčina, Petrohrad, and Chudenice.

Off-Road Environment Simulator (ORES) is a Real-time Hardware-in-the-Loop (RT-HIL) platform to simulate the dynamic response of off-road vehicles. This paper primarily focuses on the vehicle model development and validation using both field and rig testing using the ORES platform. Off-road vehicles are capable of operating on bumpy terrains where they are subjected to different resistive wheel torques due to non-unique ground friction conditions and wheel loads. If the powertrain torque output is not distributed in accordance with the resistive wheel torques, it may lead to transmission windup resulting in premature failure of various driveline components. In this study, the vehicle is driven over discrete bumps both in rig simulation and field trial. Different terrain enveloping models were evaluated namely the single point, radial-spring contact model and two-point follower (using circular and sinusoidal basis). These models were evaluated against the measured wheel acceleration responses. The two-point follower with sinusoidal basis strongly correlates with the measured responses and the ground excitations so obtained were used as inputs to a seven degree-of-freedom vehicle ride model. Ride model calculates the wheel loads and is eventually integrated with longitudinal dynamics, tire, driveline and test-rig models. Vehicle axle acceleration, wheel speed and drive torque responses are measured for validating the simulation results against field and rig trials. The field responses matches fairly well which validates the suitability of the proposed modeling approach.

Accurate topographic mapping is a critical task for various environmental applications because elevation affects hydrodynamics and vegetation distributions. UAV photogrammetry is popular in terrain modelling because of its lower cost compared to laser scanning. However, this method is restricted in vegetation area with a complex terrain, due to reduced ground visibility and lack of robust and automatic filtering algorithms. To solve this problem, this work proposed an ensemble method of deep learning and terrain correction. First, image matching point cloud was generated by UAV photogrammetry. Second, vegetation points were identified based on U-net deep learning network. After that, ground elevation was corrected by estimating vegetation height to generate the digital terrain model (DTM). Two scenarios, namely, discrete and continuous vegetation areas were considered. The vegetation points in the discrete area were directly removed and then interpolated, and terrain correction was applied for the points in the continuous areas. Case studies were conducted in three different landforms in the loess plateau of China, and accuracy assessment indicated that the overall accuracy of vegetation detection was 95.0%, and the MSE (Mean Square Error) of final DTM (Digital Terrain Model) was 0.024 m.

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.

This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework. [ABSTRACT FROM AUTHOR]

Researchers have always been in face of path loss calculation in different media for applications such as telecommunication link design. Wave propagation calculations in large spaces using the FDTD method is time-consuming and imposes a great computational burden. For this reason, to replace the classical FDTD method for wave propagation simulation and path loss calculation in large spaces, optimized methods, namely TDWP, have been provided. In this paper, the use of the TDWP method for wave propagation simulation and path loss calculation above a terrain is investigated. Longitudinal components of ground-waves are taken into account (including direct waves, ground reflections, and surface waves). Propagation space is longitudinally divided into smaller FDTD windows with finite length. The electromagnetic pulse travels through these windows from left to right to the desired point. But despite its capability in reducing computational burden and increasing processing speed, TDWP has lower precision in instantaneous field simulation and calculation of propagation coefficients, so that results obtained from FDTD and TDWP are clearly different. In this paper, some efficient methods are proposed, which yield an increase in method accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we implement real irregular terrain model in computer program by using image processing. We show how this approach can be used in simulation of E.M. wave propagation on irregular earth’s surface in a realistic manner. Some simulations are performed for implementation of longitudinal height differences over the propagation path as PEC surface .We also describe that how this approach can be used for any boundary condition in computational space. The results observed in Snapshots of the field profiles taken at different simulation times, validates capability of this method.

Geological structures, both at the surface and subsurface levels, are typically represented by means of voxel data. This model presents a major drawback: its large storage requirements. In this paper, we address this problem and propose the use of a stack-based representation for geological surface-subsurface structures. Although this representation has been mainly used for volumetric terrain visualization in previous works, it has been used as an auxiliary data structure. Therefore, our main contribution in this work is its use as a first-class representation for both processing and visualization of surface and subsurface information. The proposed solution provides real-time visualization of volumetric terrains and subsurface geological structures represented as stacks using a compact data representation in the GPU. Different GPU memory implementations of the stacks have been described, discussing the tradeoffs between performance and storage efficiency. We also introduce a novel algorithm for the calculation of the surface normal vectors using a hybrid object-image space strategy. Moreover, important features for geoscientific applications such as visualization of boreholes or geological cross sections, and selective attenuation of strata have also been implemented in a straightforward way. [ABSTRACT FROM AUTHOR]

Copyright of Ra'e Ga is the property of Ra'e Ga - O Espaco Geografico em Analise and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)