Geospatial Data Organization Methods with Emphasis on Aperture-3 Hexagonal Discrete Global Grid Systems

Amiri, Ali Mahdavi; Alderson, Troy; Samavati, Faramarz

doi:10.3138/cart.54.1.2018-0010

Cited by 16 publications

(4 citation statements)

References 34 publications

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“…However, operations frequently roll back when code normalization fails, resulting in a low efficiency [16]. A Mahdavi-Amiri [17] presented a general hierarchical indexing mechanism for hexagonal cells resulting from the refinement of a triangular spherical polyhedral. However, this encoding method is limited to the A3H DGGS, and, because the A3H DGGS rotates between different levels, there are inevitably a lot of logical judgments when decoding the codes.…”

Section: Encoding Methods Of Dggssmentioning

confidence: 99%

A GPU-Based Integration Method from Raster Data to a Hexagonal Discrete Global Grid

Zheng,

Zhou,

et al. 2024

Remote Sensing

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This paper proposes an algorithm for the conversion of raster data to hexagonal DGGSs in the GPU by redevising the encoding and decoding mechanisms. The researchers first designed a data structure based on rhombic tiles to convert the hexagonal DGGS to a texture format acceptable for GPUs, thus avoiding the irregularity of the hexagonal DGGS. Then, the encoding and decoding methods of the tile data based on space-filling curves were designed, respectively, so as to reduce the amount of data transmission from the CPU to the GPU. Finally, the researchers improved the algorithmic efficiency through thread design. To validate the above design, raster integration experiments were conducted based on the global Aster 30 m digital elevation dataDEM, and the experimental results showed that the raster integration accuracy of this algorithms was around 1 m, while its efficiency could be improved to more than 600 times that of the algorithm for integrating the raster data to the hexagonal DGGS data, executed in the CPU. Therefore, the researchers believe that this study will provide a feasible method for the efficient and stable integration of massive raster data based on a hexagonal grid, which may well support the organization of massive raster data in the field of GIS.

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Section: Encoding Methods Of Dggssmentioning

confidence: 99%

A GPU-Based Integration Method from Raster Data to a Hexagonal Discrete Global Grid

Zheng,

Zhou,

et al. 2024

Remote Sensing

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“…Our automated algorithm leverages some of the benefits of DGGSs. We use a DGGS as a common reference model to integrate the different data needed for selecting the sampling site, including satellite imagery and digital elevation models (DEMs) [32]. A DGGS not only facilitates the analysis by making the data integration transparent but also by providing efficient tools and operations including distance transform [33].…”

Section: Discrete Global Grid Systemsmentioning

confidence: 99%

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

Kazemi,

Samavati

2023

Agriculture

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Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide the farm fields into homogenous management zones that require only one soil sample. As a result, these sample points must be the best representatives of the management zones and satisfy some other geospatial conditions, such as accessibility and being away from field borders and other test points. In this paper, we introduce an algorithmic method as a framework for automatically determining locations for test points using a constrained multi-objective optimization model. Our implementation of the proposed algorithmic framework is significantly faster than the conventional GIS process. While the conventional process typically takes several days with the involvement of GIS technicians, our framework takes only 14 s for a 200-hectare field to find optimal benchmark sites. To demonstrate our framework, we use time-varying Sentinel-2 satellite imagery to delineate management zones and a digital elevation model (DEM) to avoid steep regions. We define the objectives for a representative area of a management zone. Then, our algorithm optimizes the objectives using a scalarization method while avoiding constraints. We assess our method by testing it on five fields and showing that it generates optimal results. This method is fast, repeatable, and extendable.

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“…The goal of data organization and management is to be able to quickly retrieve and query any content in the dataset [14]. DGGS itself has natural advantages, such as the use of grid coding can not only quickly achieve spatial positioning, but also can easily find child nodes and parent nodes [71,80,81]. Besides, the multilevel of DGGS can be highly consistent with the multiscale of EO data, so that the data at different scales (medium, high, and low resolutions) can be uniformly organized and managed, which provide a good data foundation for multisource data fusion analysis in the later stage.…”

Section: Eo Data Organization and Managementmentioning

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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Geospatial Data Organization Methods with Emphasis on Aperture-3 Hexagonal Discrete Global Grid Systems

Cited by 16 publications

References 34 publications

A GPU-Based Integration Method from Raster Data to a Hexagonal Discrete Global Grid

A GPU-Based Integration Method from Raster Data to a Hexagonal Discrete Global Grid

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

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

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