Regular Grid Data Research Articles

A Prediction‐Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error

AbstractWe explore an error‐bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglecting the spatial coherency of the mesh nodes. Inspired by the SZ compressor, which predicts and quantizes values in a multidimensional array, we dynamically reorganize nodal data into sequences. Each sequence starts with a seed cell; based on a predefined traversal order, the next cell is added to the sequence if the current cell can predict and quantize the nodal data in the next cell with the given error bound. As a result, one can efficiently compress the quantized nodal data in each sequence until all mesh nodes are traversed. This paper also introduces a suite of novel error metrics, namely continuous mean squared error (CMSE) and continuous peak signal‐to‐noise ratio (CPSNR), to assess compression results for unstructured mesh data. The continuous error metrics are defined by integrating the error function on all cells, providing objective statistics across nonuniformly distributed nodes/cells in the mesh. We evaluate our methods with several scientific simulations ranging from ocean‐climate models and computational fluid dynamics simulations with both traditional and continuous error metrics. We demonstrated superior compression ratios and quality than existing lossy compressors.

The Extraction of Roof Feature Lines of Traditional Chinese Village Buildings Based on UAV Dense Matching Point Clouds

Traditional Chinese buildings serve as a carrier for the inheritance of traditional culture and national characteristics. In the context of rural revitalization, achieving the 3D reconstruction of traditional village buildings is a crucial technical approach to promoting rural planning, improving living environments, and establishing digital villages. However, traditional algorithms primarily target urban buildings, exhibiting limited adaptability and less ideal feature extraction performance for traditional residential buildings. As a result, guaranteeing the accuracy and reliability of 3D models for different types of traditional buildings remains challenging. In this paper, taking Jingping Village in Western Hunan as an example, we propose a method that combines multiple algorithms based on the slope segmentation of the roof to extract feature lines. Firstly, the VDVI and CSF algorithms are used to extract the building and roof point clouds based on the MVS point cloud. Secondly, according to roof features, village buildings are classified, and a 3D roof point cloud is projected into 2D regular grid data. Finally, the roof slope is segmented via slope direction, and internal and external feature lines are obtained after refinement through Canny edge detection and Hough straight line detection. The results indicate that the CSF algorithm can effectively extract the roofs of I-shaped, L-shaped, and U-shaped traditional buildings. The accuracy of roof surface segmentation based on slope exceeds 99.6%, which is significantly better than the RANSAC algorithm and the region segmentation algorithm. This method is capable of efficiently extracting the characteristic lines of roofs in low-rise buildings within traditional villages. It provides a reference method for achieving the high-precision modeling of traditional village architecture at a low cost and with high efficiency.

Fast and Accurate Spherical Harmonic Transform for Spatio-Temporal Regular Grid Data

Fast and Accurate Spherical Harmonic Transform for Spatio-Temporal Regular Grid Data

Integrated 3D geological modeling of Sonda-Jherruck coal field, Pakistan

Despite being one of the largest coal fields in Pakistan, the Sonda-Jherruck coal resource is yet to be exploited. Exploration studies were done between 1981 and 1989 by the Geological Survey of Pakistan (GSP) and the United States Geological Survey (USGS). While much exploratory data is available, computer-aided geological modeling has not been carried out. The resource estimation was carried out using the traditional circular 891 method. The specified distance between observational points remains constant, disregarding continuity in seam thickness that leads to higher uncertainty. Fault modeling and geologic mapping were not performed in preliminary reports. Fault lines based on aerial maps are indicated. This study generated 3D solid seam models and a fault model of the deposit. Coal seams were modeled to produce spatial distribution maps for seam thickness. The overall in-place coal resources of the deposit are estimated to be 4.66 billion tons, however mineable resources are estimated to be 1.59 billion tons. The method of fault detection is based on drill hole data. Dipping was calculated using regular grid data and the unknown points were estimated using the inverse distance weighting squared method. The assessed fault zones were compared with the USGS fault lines and an apparent similarity was observed.

PointVGG: Graph convolutional network with progressive aggregating features on point clouds

Shape classification and part segmentation are essential problems in computer vision. Although convolutional neural networks have achieved excellent performance on regular grid data, such as images, they have difficulty in accurately describing the shape information and geometric representation of point clouds because point clouds are irregular and disordered. Inspired by the convolution and pooling techniques used in images, we propose point convolution (Pconv) and point pooling (Ppool) on point clouds to learn high-level features from point clouds. Pconv obtains considerable local geometric information by magnifying receptive fields gradually. Ppool solves the disorder of point clouds similar to a symmetric function. However, in contrast to the symmetric function that directly aggregates local geometric information into a vector, Ppool acquires a more detailed local geometric representation by aggregating points progressively. A novel network, namely, PointVGG, with Pconv, Ppool, and graph structure for feature learning of point clouds, is presented and applied to object classification and part segmentation. Experiments show that PointVGG achieves state-of-the-art results on challenging benchmarks of 3D point clouds.

Gravity anomaly reconstruction based on nonequispaced Fourier transform

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

Capturing the dance of the earth: PolarGlobe: Real-time scientific visualization of vector field data to support climate science

This paper introduces a streamline visualization technique that empowers PolarGlobe, an interactive, virtual globe-based, multi-dimensional scientific visualization tool to facilitate the observation and visual inspection of changes in the climate in real time. Specifically, this technique achieves effective visualization of vector-based earth science data through an automated data processing pipeline which integrates novel strategies including random seeding, finer-granularity parallelization and real-time rendering. The random seeding strategy allows for a vivid visual effect and an interactive framerate regardless of the spatial resolution in the raw dataset. The visualization algorithm is designed to be naturally parallelizable by partitioning the rendering tasks of unsteady vector field into multiple subtasks such that high-performance rendering can be realized. The platform is capable of taking either irregular or regular gridded data as input, and through the proposed data (re)projection pipeline, an automatic transformation of spatially enabled scientific data from the original data projection to the 3D globe-based virtual space is achieved. A series of experiments was conducted to identify the best configuration of rendering parameters to achieve the optimal rendering performance and visual effect. The results demonstrated the scalability and capability of the proposed PolarGlobe system to visualize big and unsteady vector flow data across different spatial and temporal scales. PolarGlobe implements former Vice President Al Gore's vision of a digital earth that enables scientists and citizens across the world to interactively study our planet. We expect the methods and techniques presented in this work to contribute significantly to both the scientific visualization and climate science communities.

Integrated 3D geological modeling of Sonda-Jherruck coal field, Pakistan

Abstract Despite being one of the largest coal fields in Pakistan, the Sonda-Jherruck coal resource is yet to be exploited. Exploration studies were done between 1981 and 1989 by the Geological Survey of Pakistan (GSP) and the United States Geological Survey (USGS). While much exploratory data is available, computer-aided geological modeling has not been carried out. The resource estimation was carried out using the traditional circular 891 method. The specified distance between observational points remains constant, disregarding continuity in seam thickness that leads to higher uncertainty. Fault modeling and geologic mapping were not performed in preliminary reports. Fault lines based on aerial maps are indicated. This study generated 3D solid seam models and a fault model of the deposit. Coal seams were modeled to produce spatial distribution maps for seam thickness. The overall in-place coal resources of the deposit are estimated to be 4.66 billion tons, however mineable resources are estimated to be 1.59 billion tons. The method of fault detection is based on drill hole data. Dipping was calculated using regular grid data and the unknown points were estimated using the inverse distance weighting squared method. The assessed fault zones were compared with the USGS fault lines and an apparent similarity was observed.

LIDAR Point Cloud Data Extraction and Establishment of 3D Modeling of Buildings

This paper takes the method of Shepard’s to deal with the original LIDAR point clouds data, and generate regular grid data DSM, filters the ground point cloud and non ground point cloud through double least square method, and obtains the rules of DSM. By using region growing method for the segmentation of DSM rules, the removal of non building point cloud, obtaining the building point cloud information. Uses the Canny operator to extract the image segmentation is needed after the edges of the building, uses Hough transform line detection to extract the edges of buildings rules of operation based on the smooth and uniform. At last, uses E3De3 software to establish the 3D model of buildings.

Optimizing the Spatial Resolution for Urban CO2 Flux Studies Using the Shannon Entropy

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO2 (FFCO2) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO2 emissions are provided in the form of a group of sector-specific vector layers with point, line, and polygon sources to support carbon cycle science and climate policy. Application to carbon cycle science, in particular, requires regular gridded data in order to link surface carbon fluxes to atmospheric transport models. However, the heterogeneity and complexity of FFCO2 sources within regular grids is sensitive to spatial resolution. From the perspective of a data provider, we need to find a balance between resolution and data volume so that the gridded data product retains the maximum amount of information content while maintaining an efficient data volume. The Shannon entropy determines the minimum bits that are needed to encode an information source and can serve as a metric for the effective information content. In this paper, we present an analysis of the Shannon entropy of gridded FFCO2 emissions with varying resolutions in four Hestia study areas, and find: (1) the Shannon entropy increases with smaller grid resolution until it reaches a maximum value (the max-entropy resolution); (2) total emissions (the sum of several sector-specific emission fields) show a finer max-entropy resolution than each of the sector-specific fields; (3) the residential emissions show a finer max-entropy resolution than the commercial emissions; (4) the max-entropy resolution of the onroad emissions grid is closely correlated to the density of the road network. These findings suggest that the Shannon entropy can detect the information effectiveness of the spatial resolution of gridded FFCO2 emissions. Hence, the resolution-entropy relationship can be used to assist in determining an appropriate spatial resolution for urban CO2 flux studies. We conclude that the optimal spatial resolution for providing Hestia total FFCO2 emissions products is centered around 100 m, at which the FFCO2 emissions data can not only fully meet the requirement of urban flux integration, but also be effectively used in understanding the relationships between FFCO2 emissions and various social-economic variables at the U.S. census block group level.

Detecting Terrain Stoniness From Airborne Laser Scanning Data †

Three methods to estimate the presence of ground surface stones from publicly available Airborne Laser Scanning (ALS) point clouds are presented. The first method approximates the local curvature by local linear multi-scale fitting, and the second method uses Discrete-Differential Gaussian curvature based on the ground surface triangulation. The third baseline method applies Laplace filtering to Digital Elevation Model (DEM) in a 2 m regular grid data. All methods produce an approximate Gaussian curvature distribution which is then vectorized and classified by logistic regression. Two training data sets consisted of 88 and 674 polygons of mass-flow deposits, respectively. The locality of the polygon samples is a sparse canopy boreal forest, where the density of ALS ground returns is sufficiently high to reveal information about terrain micro-topography. The surface stoniness of each polygon sample was categorized for supervised learning by expert observation on the site. The leave-pair-out (L2O) cross-validation of the local linear fit method results in the area under curve A U C = 0 . 74 and A U C = 0 . 85 on two data sets, respectively. This performance can be expected to suit real world applications such as detecting coarse-grained sediments for infrastructure construction. A wall-to-wall predictor based on the study was demonstrated.

Modeling nonstationary covariance function with convolution on sphere

The wide use of satellite-based instruments provides measurements in climatology on a global scale, which often have nonstationary covariance structure. The issue of modeling a spatial random fields on sphere which is stationary across longitudes is addressed with a kernel convolution approach. The observed random field is generated by convolving a latent uncorrelated random field with a class of Matérn type kernel functions. By allowing the parameters in the kernel functions to vary with locations, it is possible to generate a flexible class of covariance functions and capture the nonstationary properties. Since the corresponding covariance functions generally do not have a closed form, numerical evaluations are necessary and a pre-computation table is used to speed up the computation. For regular grid data on sphere, the circulant block property of the covariance matrix enables us to use Fast Fourier Transform (FFT) to get its determinant and inverse matrix efficiently. The proposed approach is applied to the Total Ozone Mapping Spectrometer (TOMS) data for illustration.

SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA

Abstract. Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data. Some of the existing algorithms require ancillary data such as topographic map, multiple laser returns or intensity data which may not be available, and some deal only with the single isolated outliers. This is an attempt to present an algorithm to remove both the single and cluster types of outliers, by exclusively use of the last return data. The outliers will be removed by spatial analyzing of LiDAR point clouds in a hierarchical scheme that is uses a cross-validation technique. The algorithm is tested on a dataset including many single and cluster outliers. Our algorithm can deal with both the irregular LiDAR point clouds and the regular grid data. Experimental results show that the presented algorithm almost completely detects both the single and cluster outliers, but some inlier points are wrongly removed as outlier. An accuracy assessment indicated 0.018% Error α and, 0.352% Error β that are very satisfactory.

Real-time 3D reconstruction at scale using voxel hashing

Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efficiently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both fine-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.

A geostatistical re-interpretation of gravity surveys in the Yagoua, Cameroon region

Since 1960, many gravity studies have been carried out in the Yagoua region of northern Cameroon. Gravity data was collected over a wide area of approximately 11628km2. These data are insufficient, irregular, scattered and do not efficiently permit gravity field downward and upward continuations, derivatives and other operations that might require regular gridded data. Some anomalies on the Collignon map (1968), may correlate with known geological structure but do not appear on maps by Louis (1970) and Poudjom et al. (1996). To produce regular gridded gravity data and better control anomalies due to geological structures, the kriging method was applied to a 188-data baseline. Several variogram models were tested for this purpose. It was found that a spherical variogram model is the best; it has produced a new kriging dataset of about 10,100 data and a new map of kriged Bouguer data. This map contains positive anomalies in the Maroua-Mindif and Maga areas on the Collignon (1968) map, which were not present on Louis (1970) and Poudjom et al. (1996) maps. The positive anomalies of Guibi-Doukoula and Yagoua, not separated on the Louis (1970) and Poudjom et al. (1996) maps, show up as clearly distinct as previewed by Collignon (1968). The new results can be used for subsequent gravimetric studies.

Volume Rendering of 3D Borehole Data Based on GPU

Taking 3D borehole data for research object, an algorithm flow of volume rendering based GPU is given. According to the limited and discrete characteristics of 3D borehole data, Kriging interpolation algorithm is used to construct a regular grid data model. Ray-casting algorithm based on GPU is realized with the Visualization Toolkit (VTK). The results show that after the application of volume rendering technology, it is easy to practice the functions of arbitrary section displaying, volume clipping and volume data extracting which are practicalities.

DEM EXTRACTION FROM CHANG'E-1 LAM DATA BY SURFACE SKINNING TECHNOLOGY

Abstract. DEM is a digital model or 3-D representation of a terrain's surface and it is created from terrain elevation data. The main models for DEM extraction based on Lidar data or Laser Altimeter data currently use the idea that point cloud is scattered, such as regular grid model, TIN model and contour model. Essentially, in these above methods, the discrete points are interpolated into regular grid data and irregular grid data. In fact, point cloud generated by Laser Altimeter is not totally scattered, but have some regularity. In this paper, to utilize this regularity, the proposed method adopts surface skinning technology to generate DEM from Chang'E-1 Laser Altimeter data. The surface skinning technology is widely used in the field of mechanical engineering. Surface skinning is the process of passing a smooth surface through a set of curves called sectional curves, which, in general, may not be compatible. In the process of generating section line, a need for attention is that it needs to use curvature method to get a set of characteristic points, and these feature points were used to subdivide segment; the next step is generating several curves on some key places. These curves describe the shape of the curved surface. The last step is to generate a curved surface that through these curves. The result shows that, this idea is feasible, useful and it provides a novel way to generate accurate DEM.

Extended hybrid meshing algorithm for multiresolution terrain models

Hybrid terrains are a convenient approach for the representation of digital terrain models, integrating heterogeneous data from different sources. In this article, we present a general, efficient scheme for achieving interactive level-of-detail rendering of hybrid terrain models, without the need for a costly preprocessing or resampling of the original data. The presented method works with hybrid digital terrains combining regular grid data and local high-resolution triangulated irregular networks. Since grid and triangulated irregular network data may belong to different datasets, a straightforward combination of both geometries would lead to meshes with holes and overlapping triangles. Our method generates a single multiresolution model integrating the different parts in a coherent way, by performing an adaptive tessellation of the region between their boundaries. Hence, our solution is one of the few existing approaches for integrating different multiresolution algorithms within the same terrain model, achieving a simple interactive rendering of complex hybrid terrains.

Hand-Held GPR Imaging Using Migration for Irregular Data

Handheld ground-penetrating radar (GPR) system is one of a number of technologies that has been researched as a means of improving landmine detection efficiency. But it is difficult to display landmine imaging in horizontal slices, because human being operator cannot collect regular GPR data along the survey line and the measurement positions are random. Also detection of buried landmines by GPR normally suffers from very strong clutter that will decrease the image quality. So we proposed two processing procedures to achieve hand-held GPR subsurface imaging. One processing procedure is composed of interpolation and migration algorithm. The interpolation is chosen to produce the regular grid data set. The migration is used to improve signal-clutter ratio and reconstruct subsurface imaging. In the other processing procedure we developed a simply modified migration algorithm that can directly processes irregular GPR data. Two processing procedures, migration with interpolation and modified migration without interpolation, were applied to a field experiment data respectively. Both of processing procedures can efficiently process irregular GPR data and achieve subsurface imaging of high quality. The imaging processed by migration with interpolation is used in the dual sensor, advanced landmine imaging system (ALIS), and two sets of ALIS have detected more than 80 AP-mines since 2009. Also the modified migration algorithm can save the processing time, which is important for field real-time signal processing. We concluded that to achieve clear hand-held GPR imaging, the interpolation is not necessary algorithm and the migration is the key algorithm.

Data fitting independent of grid structure using a volumic version of MPU

Volume graphics is a key technology in fields such as fluid dynamics and medical science. The visualization of volume data requires the creation of a continuous scalar field to exactly or approximately interpolate scalar values assigned to the discrete voxels. In the present paper, we propose a method that we refer to as the volumic version of the multi-level partition of unity (volumic MPU). The method approximately interpolates the scalar values with good precision to generate a scalar field that is continuous up to second-order differentiation. The volumic MPU, being independent of grid structures of input volume data, is applicable to both irregular-grid data and regular grid data. The volumic MPU can also be used as an effective data-compression technique. The speed to evaluate the created scalar field is almost as high as that of trilinear interpolation.