A Parallel Computing Approach to Spatial Neighboring Analysis of Large Amounts of Terrain Data Using Spark

Zhang, Jianbo; Ye, Zhuangzhuang; Zheng, Kai

doi:10.3390/s21020365

Cited by 9 publications

(7 citation statements)

References 26 publications

(26 reference statements)

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“…For example, MultiscaleDTM could be used to calculate terrain attributes using a 3 × 3 cell focal window, followed by the application of spectral analysis methods to vary the spatial scale by modifying the DTM prior to calculation of terrain attributes or modifying the attributes after calculation (Misiuk et al, 2021; Newman et al, 2022). Moreover, while computationally expensive, the computation time of focal operations can be improved via parallel computing (Zhang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

Ilich

Misiuk

Lecours

et al. 2023

Transactions in GIS

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Digital terrain models (DTMs) are datasets containing altitude values above or below a reference level, such as a reference ellipsoid or a tidal datum over geographic space, often in the form of a regularly gridded raster. They can be used to calculate terrain attributes that describe the shape and characteristics of topographic surfaces. Calculating these terrain attributes often requires multiple software packages that can be expensive and specialized. We have created a free, open‐source R package, MultiscaleDTM, that allows for the calculation of members from each of the five major thematic groups of terrain attributes: slope, aspect, curvature, relative position, and roughness, from a regularly gridded DTM. Furthermore, these attributes can be calculated at multiple spatial scales of analysis, a key feature that is missing from many other packages. Here, we demonstrate the functionality of the package and provide a simulation exploring the relationship between slope and roughness. When roughness measures do not account for slope, these attributes exhibit a strong positive correlation. To minimize this correlation, we propose a new roughness measure called adjusted standard deviation. In most scenarios tested, this measure produced the lowest rank correlation with slope out of all the roughness measures tested. Lastly, the simulation shows that some existing roughness measures from the literature that are supposed to be independent of slope can actually exhibit a strong inverse relationship with the slope in some cases.

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Section: Discussionmentioning

confidence: 99%

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

Ilich

Misiuk

Lecours

et al. 2023

Transactions in GIS

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“…Examples include transformation of map symbols from geographic coordinates to OpenGL Render Object [39] in a multi-threaded manner using multiple CPUs and transformation from a OpenGL Render Object to screen coordinates by using multiple GPUs (multiple cores) to perform sub-processes and thus reduce computing time. Although parallel computing has been widely used in GIS software [40][41][42], this paper proposes a combined parallel computing method of multi CPU and GPU multi-core to accelerate the conversion of geographic coordinate data.…”

Section: Ar-gis Map Symbol Parallel Processing Frameworkmentioning

confidence: 99%

An Efficient, Platform-Independent Map Rendering Framework for Mobile Augmented Reality

Huang

Wang

et al. 2021

IJGI

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With the extensive application of big spatial data and the emergence of spatial computing, augmented reality (AR) map rendering has attracted significant attention. A common issue in existing solutions is that AR-GIS systems rely on different platform-specific graphics libraries on different operating systems, and rendering implementations can vary across various platforms. This causes performance degradation and rendering styles that are not consistent across environments. However, high-performance rendering consistency across devices is critical in AR-GIS, especially for edge collaborative computing. In this paper, we present a high-performance, platform-independent AR-GIS rendering engine; the augmented reality universal graphics library (AUGL) engine. A unified cross-platform interface is proposed to preserve AR-GIS rendering style consistency across platforms. High-performance AR-GIS map symbol drawing models are defined and implemented based on a unified algorithm interface. We also develop a pre-caching strategy, optimized spatial-index querying, and a GPU-accelerated vector drawing algorithm that minimizes IO latency throughout the rendering process. Comparisons to existing AR-GIS visualization engines indicate that the performance of the AUGL engine is two times higher than that of the AR-GIS rendering engine on the Android, iOS, and Vuforia platforms. The drawing efficiency for vector polygons is improved significantly. The rendering performance is more than three times better than the average performances of existing Android and iOS systems.

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“…Compared with a single computer, distributed storage (e.g., Hadoop distributed file system (HDFS), HBase) and computational technologies (e.g., MapReduce, Spark) use the storage and computational resources of clusters and show tremendous advantages when data increases dramatically. Therefore, they are extensively used in the storage [19][20][21], calculation [22,23], segmentation [24], and path planning of massive remote sensing data. Wang et al used the MapReduce-based distributed parallel Dijkstra algorithm to solve the shortest path problem.…”

Section: Introductionmentioning

confidence: 99%

A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed Tile Pyramid Strategy

Hong

Tong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In lunar exploration missions, path planning for lunar rovers using digital elevation models (DEMs) is currently a hot topic in academic research. However, research on path planning using large-scale DEMs has rarely been discussed, owing to the low time efficiency of existing algorithms. Therefore, in this study, we propose a fast path-planning method using a distributed tile pyramid strategy and an improved A* algorithm. The proposed method consists of three main steps. First, the tile pyramid is generated for the large lunar DEM and stored in Hadoop distributed file system. Second, a distributed path-planning strategy based on tile pyramid (DPPS-TP) is used to accelerate path-planning tasks on large-scale lunar DEMs using Spark and Hadoop. Finally, an improved A* algorithm was proposed to improve the speed of the pathplanning task in each tile. The method was tested using lunar DEM images. Experimental results demonstrate that: (1) in a single-machine serial strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed A* algorithm for Open List and Closed List with random access feature (OC-RA-A* algorithm) is 3.59 times faster than the traditional A* algorithm in long-distance path planning tasks; (2) compared to the distributed parallel computation strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed DPPS-TP based on tile pyramid DEM is 113.66 times faster in the long-range path planning task.

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A Parallel Computing Approach to Spatial Neighboring Analysis of Large Amounts of Terrain Data Using Spark

Cited by 9 publications

References 26 publications

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

An Efficient, Platform-Independent Map Rendering Framework for Mobile Augmented Reality

A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed Tile Pyramid Strategy

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