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Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? 总被引：1，自引：0，他引：1
《International Journal of Digital Earth》2013,6(4):305-329
Abstract The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC). 相似文献
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GeoPW: Laying Blocks for the Geospatial Processing Web 总被引：2，自引：0，他引：2
Peng Yue Jianya Gong Liping Di Jie Yuan Lizhi Sun Ziheng Sun Qian Wang 《Transactions in GIS》2010,14(6):755-772
Recent advances in Web‐related technologies have significantly promoted the wide sharing and integrated analysis of distributed geospatial data. Geospatial applications often involve diverse sources of data and complex geoprocessing functions. Existing Web‐based GIS focuses more on access to distributed geospatial data. In scientific problem solving, the ability to carry out geospatial analysis is essential to geoscientific discovery. This article presents the design and implementation of GeoPW, a set of services providing geoprocessing functions over the Web. The concept of the Geospatial Processing Web is discussed to address the geoprocessing demands in the emerging information infrastructure, and the role of GeoPW in establishing the Geospatial Processing Web is identified. The services in GeoPW are implemented by developing middleware that wraps legacy GIS analysis components to provide a large number of geoprocessing utilities over the Web. These services are open and accessible to the public, and they support integrated geoprocessing on the Web. 相似文献
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传统的单机环境和封闭式网络环境由于有限的资源利用能力, 难以充分支持分散地学数据、模型等资源的共享与应用集成。基于网络环境的信息交换特点, 提出了分布式地理空间模型共享的服务体系。该体系以数据、模型、元数据等互操作要素为核心, 通过网络将数据、模型等网络节点进行开放式耦合。针对地理空间模型服务的互操作问题, 提出了分布式环境下的模型共享服务交互接口, 该接口定义了模型服务元数据、模型服务的交互操作、模型服务的通讯方式等交互规则, 尽可能地降低模型服务与模型终端之间在数据交换、功能调用等方面的互操作困难。为了降低将模型共享为模型服务的实现难度, 设计和开发了地理空间模型共享平台, 并介绍了在该平台上发布地理空间模型的2种方法。最后介绍了研究成果在Prairie生态模型共享方面的应用实践。 相似文献
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Caitlin Haedrich Vaclav Petras Anna Petrasova Stefan Blumentrath Helena Mitasova 《Transactions in GIS》2023,27(3):686-702
Open education materials are critical for the advancement of open science and the development of open-source software. These accessible and transparent materials provide an important pathway for sharing both standard geospatial analysis workflows and advanced research methods. Computational notebooks allow users to share live code with in-line visualizations and narrative text, making them a powerful interactive teaching tool for geospatial analytics. Specifically, Jupyter Notebooks are quickly becoming a standard format in open education. In this article, we introduce a new GRASS GIS package, grass.jupyter , that enhances the existing GRASS Python API to allow Jupyter Notebook users to easily manage and visualize GRASS data including spatiotemporal datasets. While there are many Python-based geospatial libraries available for use in Jupyter Notebooks, GRASS GIS has extensive geospatial functionality including support for multi-temporal analysis and dynamic simulations, making it a powerful teaching tool for advanced geospatial analytics. We discuss the development of grass.jupyter and demonstrate how the package facilitates teaching open-source geospatial modeling with a collection of Jupyter Notebooks designed for a graduate-level geospatial modeling course. The open education notebooks feature spatiotemporal data visualizations, hydrologic modeling, and spread simulations such as the spread of invasive species and urban growth. 相似文献
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《International Journal of Digital Earth》2013,6(3):275-289
Abstract In recent years, geographical information systems have been employed in a wide variety of application domains, and as a result many research efforts are being devoted to those upcoming problems. Geospatial data security, especially access control, has attracted increased research interests within the academic community. The tendency towards sharing and interoperability of geospatial data and applications makes it common to acquire and integrate geospatial data from multiple organisations to accomplish a complex task. Meanwhile, many organisations have the requirement for securing access to possessed sensitive or proprietary geospatial data. In this heterogeneous and distributed environment, consistent access control functionality is crucial to promote controlled accessibility. As an extension of general access control mechanisms in the IT domain, the mechanism for geospatial data access control has its own requirements and characteristics of granularity and geospatial logic. In this paper, we address several fundamental aspects concerning the design and implementation of an access control system for geospatial data, including the classification, requirements, authorisation models, storage structures and management approaches for authorisation rules, matching and decision-making algorithms between authorisation rules and access requests, and its policy enforcement mechanisms. This paper also presents a system framework for realising access control functionality for geospatial data, and explain access control procedures in detail. 相似文献
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Big geospatial data is an emerging sub‐area of geographic information science, big data, and cyberinfrastructure. Big geospatial data poses two unique challenges. First, raster and vector data structures and analyses have developed on largely separate paths for the last 20 years. This is creating an impediment to geospatial researchers seeking to utilize big data platforms that do not promote heterogeneous data types. Second, big spatial data repositories have yet to be integrated with big data computation platforms in ways that allow researchers to spatio‐temporally analyze big geospatial datasets. IPUMS‐Terra, a National Science Foundation cyberInfrastructure project, addresses these challenges by providing a unified framework of integrated geospatial services which access, analyze, and transform big heterogeneous spatio‐temporal data. As IPUMS‐Terra's data volume grows, we seek to integrate geospatial platforms that will scale geospatial analyses and address current bottlenecks within our system. However, our work shows that there are still unresolved challenges for big geospatial analysis. The most pertinent is that there is a lack of a unified framework for conducting scalable integrated vector and raster data analysis. We conducted a comparative analysis between PostgreSQL with PostGIS and SciDB and concluded that SciDB is the superior platform for scalable raster zonal analyses. 相似文献
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多尺度邻域特征下的机载LiDAR点云电力线分类 总被引：1，自引：0，他引：1
利用机载激光雷达技术三维测量精度高且获取快速的优点进行电力线自动分类提取已成为点云数据处理与电力应用的重要领域。针对电力线分类模型的自动化和高精度需求，本文提出了基于三维多尺度邻域特征的机载LiDAR点云电力线分类提取模型框架，主要包括4个步骤：电力线候选点滤波、多尺度邻域类型选取、形状结构特征提取和支持向量机分类。通过对2个复杂城市区域的试验数据集和8种不同邻域类型的详细结果对比分析，发现基于多尺度圆球邻域形状结构特征的分类模型结果准确率、召回率和质量分别达到97%、94%和93%，同时整体处理时间在2个试验数据中分别从366、256 s减少到274、160 s。试验结果表明，该方法在多种复杂城市场景中能够实现机载LiDAR数据的电力线较高精度分类提取。 相似文献
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现代信息技术的变革使网络技术与GIS的结合日益紧密 ,从而促进了开放GIS技术的研究。开放式GIS研究的目的是提供一套具有开放界面规范的通用组件 ,开发者根据这些规范开发出交互式组件 ,从而实现不同种类地理数据和地理处理方法间的透明访问。文中主要讨论了基于CORBA的开放式GIS的网络系统构建技术 ,使用户能在分布式的多服务器且存在异构的网络环境中共享地理空间数据。 相似文献
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Thomas C. Hammond Alec Bodzin David Anastasio Breena Holland Kate Popejoy Dork Sahagian 《制图学和地理信息科学》2018,45(4):305-318
Many barriers exist to K–12 classroom teachers’ adoption and implementation of geospatial technologies with their students. To address this circumstance, we have developed and implemented a geospatial curriculum approach to promote teachers’ professional growth with curriculum-linked professional development (PD) to support the adoption of socio-environmental science investigations (SESI) in an urban school environment that includes reluctant learners. SESI focus on social issues related to environmental science. The pedagogy is inquiry-driven, with students engaged in map-based mobile data collection and subsequent analysis with Web-based dynamic mapping software to answer open-ended questions. Working with four science and social studies teachers, we designed and implemented a sequence of three locally oriented, geospatial inquiry projects that were implemented with 140 9th grade students. We investigated how the geospatial curriculum approach impacted the teachers’ geospatial pedagogical content knowledge (PCK), their cartographic practices, and promoted geospatial thinking and analysis skills with their students. Findings revealed strong growth in teachers’ geospatial PCK, increased map use by teachers, use of maps as media for inquiry and not didactic instruction, and modeling to guide students’ geospatial analysis using GIS. Implications for PD to promote teachers’ geospatial PCK and in-class cartographic practices are discussed. 相似文献
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The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework. 相似文献
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针对人们对地理空间数据迫切需求,介绍了多源地理空间矢量数据产生的管理与技术原因及其5种表现形式,提出了多源地理空间矢量数据集成与融合的概念及其相互关系。论述了地理数据模型的融合、地理要素语义的融合和地理数据投影和坐标系的统一是多源地理空间矢量数据集成与融合基本理论与方法。给出了实现多源数据集成的数据格式转换、数据互操作和直接数据访问方法。最后,讨论了地理要素几何位置的融合,以及通过地理要素语义融合消除地理要素数据描述和属性差异的矢量数据融合方法。 相似文献
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Fabio Gomes de Andrade Cláudio de Souza Baptista Fabio Luiz Leite Jr 《Transactions in GIS》2011,15(5):707-722
Nowadays, Spatial Data Infrastructures (SDIs) play an important role in government agencies, at different levels: global, national, and local. They aim to improve the management and sharing of geospatial data. Nonetheless, these SDIs have been developed as information islands, in which a user's query is compared to metadata described only in their own catalog services. The lack of interaction among SDIs limits the potential of these infrastructures in providing geospatial data to a larger audience. This article presents a distributed architecture, based on a federation of SDIs which interact among themselves, using query propagation. This propagation facilitates data discovery and sharing. We also describe a distributed query processing service used to enable the resource discovery in distributed infrastructures. 相似文献
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Christopher Bone Alan Ager Ken Bunzel Lauren Tierney 《International Journal of Digital Earth》2016,9(1):47-62
The volume of publically available geospatial data on the web is rapidly increasing due to advances in server-based technologies and the ease at which data can now be created. However, challenges remain with connecting individuals searching for geospatial data with servers and websites where such data exist. The objective of this paper is to present a publically available Geospatial Search Engine (GSE) that utilizes a web crawler built on top of the Google search engine in order to search the web for geospatial data. The crawler seeding mechanism combines search terms entered by users with predefined keywords that identify geospatial data services. A procedure runs daily to update map server layers and metadata, and to eliminate servers that go offline. The GSE supports Web Map Services, ArcGIS services, and websites that have geospatial data for download. We applied the GSE to search for all available geospatial services under these formats and provide search results including the spatial distribution of all obtained services. While enhancements to our GSE and to web crawler technology in general lie ahead, our work represents an important step toward realizing the potential of a publically accessible tool for discovering the global availability of geospatial data. 相似文献
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Crowdsourcing geospatial data 总被引：6，自引：0，他引：6
In this paper we review recent developments of crowdsourcing geospatial data. While traditional mapping is nearly exclusively coordinated and often also carried out by large organisations, crowdsourcing geospatial data refers to generating a map using informal social networks and web 2.0 technology. Key differences are the fact that users lacking formal training in map making create the geospatial data themselves rather than relying on professional services; that potentially very large user groups collaborate voluntarily and often without financial compensation with the result that at a very low monetary cost open datasets become available and that mapping and change detection occur in real time. This situation is similar to that found in the Open Source software environment.We shortly explain the basic technology needed for crowdsourcing geospatial data, discuss the underlying concepts including quality issues and give some examples for this novel way of generating geospatial data. We also point at applications where alternatives do not exist such as life traffic information systems. Finally we explore the future of crowdsourcing geospatial data and give some concluding remarks. 相似文献
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《International Journal of Digital Earth》2013,6(6):451-475
A spatial web portal (SWP) provides a web-based gateway to discover, access, manage, and integrate worldwide geospatial resources through the Internet and has the access characteristics of regional to global interest and spiking. Although various technologies have been adopted to improve SWP performance, enabling high-speed resource access for global users to better support Digital Earth remains challenging because of the computing and communication intensities in the SWP operation and the dynamic distribution of end users. This paper proposes a cloud-enabled framework for high-speed SWP access by leveraging elastic resource pooling, dynamic workload balancing, and global deployment. Experimental results demonstrate that the new SWP framework outperforms the traditional computing infrastructure and better supports users of a global system such as Digital Earth. Reported methodologies and framework can be adopted to support operational geospatial systems, such as monitoring national geographic state and spanning across regional and global geographic extent. 相似文献
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关于空间数据质量标准的若干问题 总被引：7，自引：0，他引：7
引入空间数据质量维的概念,在比较分析几个标准化组织提出的空间数据质量内容组成的基础上,结合我国的特点和现实要求,提出了一套可供参考的空间数据质量元素和子元素。分析了空间数据质量的3种评价模式,并将ISO/TC211的加权平均法和我国的缺陷扣分法结合,提出了基于加权平均的缺陷扣分评价方法,给出了这种方法的实现流程。 相似文献
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Geospatial Agents, Agents Everywhere . . . 总被引：1，自引：0，他引：1
The use of the related terms “agent‐based”, “multi‐agent”, “software agent” and “intelligent agent” have witnessed significant growth in the Geographic Information Science (GIScience) literature in the past decade. These terms usually refer to both artificial life agents that simulate human and animal behavior and software agents that support human‐computer interactions. In this article we first comprehensively review both types of agents. Then we argue that both these categories of agents borrow from Artificial Intelligence (AI) research, requiring them to share the characteristics of and be similar to AI agents. We also argue that geospatial agents form a distinct category of AI agents because they are explicit about geography and geographic data models. Our overall goal is to first capture the diversity of, and then define and categorize GIScience agent research into geospatial agents, thereby capturing the diversity of agent‐oriented architectures and applications that have been developed in the recent past to present a holistic review of geospatial agents. 相似文献