Hexagonal connectivity maps for Digital Earth

Mahdavi-Amiri, Ali; Harrison, Erika; Samavati, Faramarz

doi:10.1080/17538947.2014.927597

Cited by 48 publications

(40 citation statements)

References 17 publications

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“…Although it can satisfy people s direct cognition, when facing large-scale or global data and applications, the data obtained by projection will have serious deformation problems, especially in polar or high latitudes [87]. The global discrete grid system is a spherical mathematical model, which is equivalent at any point on the Earth and is more suitable for the analysis and visualization of global datasets [60,88]. DGGS is a spatiotemporal framework, not only conducive to fusion analysis of remote sensing data with different resolutions and multiple spectrums, but also able to achieve seamless integration with other geographic information system (GIS) data, which will improve the accuracy of data analysis and data utilization [89].…”

Section: Fusion Analysis Of Global-or Regional-scale Eo Datamentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment.Remote Sens. 2020, 12, 62 2 of 15 (CEOS), over 500 EO satellites have been launched in the last half-century, and more than 150 satellites will be launched in the next 12 years [2,3]. China has launched more than 60 satellites since 1970 for comprehensive observation of the Earth's systems, including HuanJing (HJ), FengYun (FY), China-brazil earth resource satellite (CBERS), and GaoFen (GF) series. From the European Space Agency (ESA), in terms of Sentinel family, a total of 50,964,670 products had been downloaded by users since the start of data access operations, with a total data volume of 41. 35 PB [4]. The big Earth observation data (BEOD) has gradually promoted the development of global industries, research institutions, and application sectors, which has had a profound impact on the study of the Earth system [5,6], contributing to human activities, environmental monitoring, and climate changes, and also provided abundant data resource for the construction of digital Earth [7][8][9].BEOD also poses challenges to uses in terms of problem complexity, automatic analysis, and processing efficiency [10][11][12][13]. Fortunately, the rapid advancement of cloud computing technology in recent years provides strong computing power, especially for the efficiency of big geospatial data management and process...

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Section: Fusion Analysis Of Global-or Regional-scale Eo Datamentioning

confidence: 99%

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Yao

Xia

et al. 2019

Remote Sensing

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“…A favorable alternative to a Cartesian grid is the so-called geodesic grid which is a spherical grid that is often used in climate simulations. [37,38] The k·p band structure Hamiltonian can written as a sum which distinguishes the involved symmetries [39] H =Ĥ sphere +Ĥ cube +Ĥ tetra .…”

Section: Theoretical Approach and Numerical Challengesmentioning

confidence: 99%

Anisotropic excitons and their contributions to shift current transients in bulk GaAs

2017

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Shift current transient are obtained for near band gap excitation of bulk GaAs by numerical solutions of the semiconductor Bloch equations in a basis obtained from a 14 band k·p model of the band structure. This approach provides a transparent description of the optically induced excitations in terms of interband, intersubband, and intraband excitations which enables a clear distinction between different contributions to the shift current transients and fully includes resonant as well as off-resonant processes. Using a geodesic grid in reciprocal space in our numerical solutions, we are able to include the electron-hole Coulomb attraction in combination with our anisotropic three-dimensional band structure. We obtain an excitonic absorption peak and an enhancement of the continuum absorption and demonstrate that the excitonic wave function contains a significant amount of anisotropy. Optical excitation at the excitonic resonance generates shift current transients of significant strength, however, due to the electron-hole attraction the shift distance is smaller than for above band gap excitation. We thus demonstrate that our approach is able to provide important information on the ultrafast electron dynamics on the atomic scale.

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“…Beside traditional PCs produced mostly by 3D scanners [9], a set of points can also represent general spatial information [10] such as location, GPS coordinates on the map [11], swarm particles [12], nodes of graph generated by visualization algorithms [13], etc. We focus on 2D point clouds as they are highly investigated in the areas such as data mining [2,3,10], computer graphics [14,15], image analysis [16][17][18], geographic information systems (GISs) [19][20][21][22][23], sensor networks [24,25], triangulation [26][27][28][29] and data visualization [12]. In the mentioned application fields, the point indexing/clustering algorithms are crucial, e.g., for efficient organization of data in the memory, analyzing their properties, visualization of clusters, computation of forces in particle systems and searching near neighbors.…”

Section: Introductionmentioning

confidence: 99%

“…Those methods serve to decompose and represent images with pixels having positive coordinates, but they do not offer any general method for real points indexing and querying. Several papers discussed the problems of different grid systems and their mutual conversion to combine their best properties [21][22][23], but they all miss the correct hierarchical 1-to-7 refinement in the hexagonal grid ( Figure 2) for a correct hexagonal SFC. During the last years, the hexagons have gained increased interest in scientific areas including geographic information systems [19][20][21][22][23]40,41], data clustering and querying [36,38,39,42,43], data visualization [44,45], computer graphics [46] and computer or sensor networks [24,25].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Hexagonal Clustering and Indexing

et al. 2019

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Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method.

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Hexagonal connectivity maps for Digital Earth

Cited by 48 publications

References 17 publications

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges

Anisotropic excitons and their contributions to shift current transients in bulk GaAs

Hierarchical Hexagonal Clustering and Indexing

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