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本文对SpacEyes3D平台下的三维可视化技术进行了探讨,研究了在SpacEyes3D平台下建立矿山三维GIS平台的方法,并基于c#开发语言应用插件编程建立了矿山三维GIS平台。 相似文献
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随着全球四大卫星导航系统格局的成型,卫星定位系统已从单系统模式发展为如今多系统、多频率融合定位、交互操作的模式。在分析多系统精密单点定位模型及各误差项处理策略的基础上,利用RTKLIB进行GPS,GLONASS,GALILEO,BDS多系统融合精密单点定位试验,并分析其动/静态定位性能。实验结果表明:在单系统空间几何构型较差的情况下,多系统融合精密单点定位较单GPS定位精度可提高20%~40%,收敛时间可缩短35%~50%;在截止高度角超过40°的情况下,单系统会因可见卫星数量不足而无法完成连续定位,而多系统仍能实现高精度的连续定位。这在城区、山区或卫星遮蔽较严重的不利环境中有重要的利用价值。 相似文献
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Fei Hu Yongyao Jiang Yun Li Weiwei Song Daniel Q. Duffy 《International Journal of Digital Earth》2020,13(3):410-428
ABSTRACTEarth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency. 相似文献
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Li‐Yang Xiong Ru‐Qiao Jiang Qing‐Hui Lu Bi‐Sheng Yang Fa‐Yuan Li Guo‐An Tang 《Transactions in GIS》2019,23(2):259-274
Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data. 相似文献