HexTile
A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark
Dados Bibliográficos
| ID | 22032492 |
|---|---|
| Autores | Xiaochuang Yao (0000-0001-8068-9415, Ministry of Natural Resources, autor correspondente), Guojiang Yu (0000-0003-4236-8447, China Agricultural University), Guoqing Li (0000-0001-6799-372X, Chinese Academy of Sciences), Shuai Yan (0000-0001-9211-8759, China Agricultural University), Long Zhao (0000-0002-1012-0159, Chinese Academy of Sciences), Dehai Zhu (Ministry of Natural Resources) |
| Idioma | EN |
| Referências citadas | 21 |
The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology
Algorithm · Big data · Computational science · Data mining · Database · Distributed computing · Grid · Image stitching · Scalability · Visualization · Computer Science · Data Management and Algorithms · Graph Theory and Algorithms · Land Use and Ecosystem Services · Mathematics · Artificial Intelligence
| Altamente citado | Não |
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