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Peng Yue Fan Gao Boyi Shangguan Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274
ABSTRACT High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning. 相似文献
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在传统方式下,ArcGIS地理处理工具的执行过程不能充分利用高性能多核计算机的全部运算能力处理日益增加的地理数据,导致数据处理效率低下。本文在分析地理处理工具特点的基础上,充分利用Python语言的并行编程特点,构建具有通用性的地理处理任务并行运行解决方案。结合ArcGIS软件自身的特质,有效解决了并行运行所带来的数据竞争、数据共享与进程通讯等问题,达到了一定硬件环境条件下ArcGIS工具执行效率最大化的目的。通过典型地理处理任务中不同运行方式效率的对比测试与分析,证明了并行运行的有效性。 相似文献
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Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources. 相似文献
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基于Python的ArcGIS地理数据批处理 总被引：1，自引：0，他引：1
ArcGIS地理处理工具一般只针对单个数据集执行,而运用Python脚本语言可以实现地理数据的批处理。本文以原始DEM影像插值生成特定空间分辨率的DEM影像为例,给出数据批处理的具体实现过程。 相似文献
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《International Journal of Digital Earth》2013,6(4):404-425
Abstract The emergence of Cloud Computing technologies brings a new information infrastructure to users. Providing geoprocessing functions in Cloud Computing platforms can bring scalable, on-demand, and cost–effective geoprocessing services to geospatial users. This paper provides a comparative analysis of geoprocessing in Cloud Computing platforms – Microsoft Windows Azure and Google App Engine. The analysis compares differences in the data storage, architecture model, and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms; emphasizes the importance of virtualization; recommends applications of hybrid geoprocessing Clouds, and suggests an interoperable solution on geoprocessing Cloud services. The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern, once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies. The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms. The tested services are developed using geoprocessing algorithms from different vendors, GeoSurf and Java Topology Suite. The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services. 相似文献
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Aengus McCullough Philip James Stuart Barr 《International journal of geographical information science》2013,27(10):1939-1957
The geospatial sensor web is set to revolutionise real-time geospatial applications by making up-to-date spatially and temporally referenced data relating to real-world phenomena ubiquitously available. The uptake of sensor web technologies is largely being driven by the recent introduction of the OpenGIS Sensor Web Enablement framework, a standardisation initiative that defines a set of web service interfaces and encodings to task and query geospatial sensors in near real time. However, live geospatial sensors are capable of producing vast quantities of data over a short time period, which presents a large, fluctuating and ongoing processing requirement that is difficult to adequately provide with the necessary computational resources. Grid computing appears to offer a promising solution to this problem but its usage thus far has primarily been restricted to processing static as opposed to real-time data sets. A new approach is presented in this work whereby geospatial data streams are processed on grid computing resources. This is achieved by submitting ongoing processing jobs to the grid that continually poll sensor data repositories using relevant OpenGIS standards. To evaluate this approach a road-traffic monitoring application was developed to process streams of GPS observations from a fleet of vehicles. Specifically, a Bayesian map-matching algorithm is performed that matches each GPS observation to a link on the road network. The results show that over 90% of observations were matched correctly and that the adopted approach is capable of achieving timely results for a linear time geoprocessing operation performed every 60 seconds. However, testing in a production grid environment highlighted some scalability and efficiency problems. Open Geospatial Consortium (OGC) data services were found to present an IO bottleneck and the adopted job submission method was found to be inefficient. Consequently, a number of recommendations are made regarding the grid job-scheduling mechanism, shortcomings in the OGC Web Processing Service specification and IO bottlenecks in OGC data services. 相似文献
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目前,基础测绘生产已从传统生产方式过渡到以地理对象数据库为存储方式的信息化测绘,其成果也多采用ArcGIS软件的Geodatabase技术进行存储。基于Geodatabase对空间数据的管理特点,如何快速高效地实现对大批量的基础地理信息数据的质量评价工作,是质检人员在新的基础测绘生产模式阶段面临的一个重要问题。为此,本文提出利用ArcGIS软件自带的Geoprocessing 技术对基础地理信息数据库中的空间数据拓扑关系进行检查,通过介绍Geoprocessing技术的概念及其开发框架,比较Geoprocessing 框架中ModelBuilder、Python、ArcOb-jects三种开发模式的不同特点,结合以往工作积累以及用户使用习惯,最终选择使用Python方式开发自定义的Geoprocessing Tools 和使用VBA开发用户界面相结合的模式定制基础地理信息数据库的图形检查工具,最终实现空间数据拓扑关系检查工作的通用化和自动化,并以浙江省1∶10000比例尺基础地理信息数据库的数据质量评价验收为例,说明Geoprocessing技术在空间数据库质量检查中的实际应用。 相似文献
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ArcGIS系列软件提供了多种形式的地理处理工具,模型构建器和Python脚本是其中的典型代表。本文以地形图数据库批量图幅裁切问题为例,按照数据准备、单图框裁切、重复单图框裁切的思路将这一地理处理过程分解,用模型构建器和Python脚本两种工具分别实现了矢量数据批量裁切的功能,详细地介绍了两种方法的技术要点及细节,获得了预期的结果数据,并比较分析了两者的优劣势及相互联系。 相似文献
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针对地学空间信息基础设施迫切需要能够集成多源地理信息处理软件包与分布式空间信息网络服务的工作流软件,以帮助用户构建复杂的处理流程,实现从数据、信息到知识转换的现状,该文提出了一种分布式多源处理功能集成的地学工作流脚本方法。利用脚本来连接不同地理信息处理软件中的功能,并通过工作流自动生成技术来构建复杂的空间信息处理流程,以实现对数据处理与分析能力的提高。通过扩展地学工作流建模工具——GeoJModelBuilder,在空间信息服务组合的基础上集成地理信息处理软件,实现了多源处理功能集成的原型系统,并以从数字高程模型中提取水文模型为例来证明该方法的可行性。 相似文献
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