An Efficient Tile-Pyramids Building Method for Fast Visualization of Massive Geospatial Raster Datasets

Guo, Ning; Xiong, Wei; Wu, Qunyong; Jing, Ning

doi:10.4316/aece.2016.04001

Cited by 13 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(12) Although a large number of studies are focused on rapid image splicing, (13)(14)(15) the splicing process requires a large number of computations and a large amount of time owing to a large number of images and a significant amount of image overlapping. (11) Therefore, to improve the efficiency of building a tile pyramid, many studies focus on the optimization of the parallel strategy of tile cutting. Liu et al (5) divided pyramid building into processes by adopting the secondary data processing strategy and achieved parallel tile building and outputting.…”

Section: Building Methods Based On Image Fusionmentioning

confidence: 99%

“…According to processing methods for massive time-series images, the building of a tile pyramid can be done in one of the three ways: building based on image fusion, (2)(3)(4)(5)(6) building based on a mosaic dataset, and building based on map plotting. (7)(8)(9)(10)(11) Among these methods, building based on map plotting cannot be accomplished in a normal environment owing to its reliance on a rendering engine for map plotting and its extensive demands on the computing environment. Therefore, currently, the most commonly used methods for tile pyramid building are those based on image fusion and those based on a mosaic dataset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Liu¹,

Sun²

2023

Sensors and Materials

View full text Add to dashboard Cite

The efficient building of tile pyramids is the basis for achieving efficient visualization of massive remote sensing images and network publishing. With the increasing number of images and amount of data from images, the existing method leads to excessive overall time consumption and low efficiency of the building process due to the poor quality of building algorithms and parallel strategies. In this study, an optimization strategy for building a tile pyramid of massive remote sensing images was proposed. This method comprehensively used the hierarchical blank tiles of remote sensing images and the strategy of multithreading parallel pyramid construction to reorganize the existing tile pyramid construction process. The experimental results showed that the method proposed in this study was less time-consuming and had an obviously improved overall efficiency compared with that of the existing method of building a tile pyramid using massive remote sensing images. Therefore, this method is more advantageous to use than previous methods.

show abstract

Section: Building Methods Based On Image Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Liu¹,

Sun²

2023

Sensors and Materials

View full text Add to dashboard Cite

show abstract

“…As an effective method for spatiotemporal data analysis, visualization can intuitively express the meaning of big data. The map tile is a widely used visualization solution for remote sensing images, which are subdivided into pieces of tiles organized as a pyramid model for display [1]. However, most of the existing map tiles are constructed based on a plane, which cannot avoid a series of problems caused by map projections, such as area or length deformation, and missing high latitude data.…”

Section: Introductionmentioning

confidence: 99%

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

Yao

et al. 2023

IJGI

View full text Add to dashboard Cite

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.

show abstract

“…connectivity index of the cultivated map is often related to the number of patches, and the larger the data volume is, the more complex the calculation. The larger the amount of farmland plot data, the better the data rendering effect, and then, the traditional raster tile technology using static (fixed resolution) cache has been unable to meet the requirements of large-scale vector data multiscene application (Guo et al, 2016;Wan et al, 2016). For example, map rendering should be carried out according to different field contents for different datasets such as the national map spot quality.…”

mentioning

confidence: 99%

Map vector tile construction for arable land spatial connectivity analysis based on the Hadoop cloud platform

Ma,

Zhang

2023

Front. Earth Sci.

View full text Add to dashboard Cite

The concentrated connection of arable land is one of the important indicators reflecting the quality of cultivated land, and large-scale arable land blocks are more conducive to agricultural mechanization operation, thereby improving the land use efficiency. However, the calculation of farmland connectivity is essentially a large-scale calculation of spatial vector data, especially for the national or global farmland patch data. This article proposes a framework for calculating farmland connectivity based on spatial vector map tiles and parallelizes the algorithm based on the Hadoop cloud platform. The framework is based on the tile pyramid model and uses the Douglas–Peucker algorithm to simplify the data to meet the needs of rapid display of large-scale data under multi-scale. The consistency and integrity of the front display of vector tiles are ensured using the setting tile buffer. Meanwhile, the parallelization of the vector tile construction algorithm is realized based on the MapReduce programming mode. Finally, the effectiveness and usability of this framework were verified through the calculation of patch connectivity on the tillage map. Experiments show that the algorithm can not only meet the rapid construction requirements of large-scale vector tile data but also support the cultivated land spatial connectivity analysis and greatly improve the efficiency of supporting data calculation.

show abstract

An Efficient Tile-Pyramids Building Method for Fast Visualization of Massive Geospatial Raster Datasets

Cited by 13 publications

References 7 publications

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

Optimization Strategy for Building Tile Pyramid of Massive Remote Sensing Images

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

Map vector tile construction for arable land spatial connectivity analysis based on the Hadoop cloud platform

Contact Info

Product

Resources

About