国土资源电子政务中GIS与工作流的集成应用研究

图文一体化已成为电子政务信息化建设的一项基本要求。基于模型级图文一体化的集成方式基本上解决了电子政务建设过程中GIS图形系统和工作流审批系统的开发相对独立的问题。模型级图文一体化的集成方式代表了GIS与工作流技术集成的发展趋势。本文以江苏省国土资源电子政务基础平台中GIS与工作流的集成为实例,研究了该电子政务平台中GIS的体系结构及GIS功能的划分,并从数据集成、用户权限角色集成、功能集成、用户界面集成等四个方面对国土资源电子政务平台实现图文的一体化。并在模型层次对GIS与工作流的集成进行了初步尝试。     

基于工作流技术的社区管理系统的设计

分析了基于工作流技术管理系统软件的优势,阐述了数字社区的应用软件和工作流引擎整合的通用途径,并设计了一个基于工作流的应用系统。实验表明,结合知识库、规则库的应用软件可以极大地提升数字社区管理软件的应用水平。     

GIS工作流系统的长事务处理研究

分析了Sagas事务模型及空间数据版本树技术,设计了一种灵活的长事务补偿控制方案,为集成GIS,MIS及OA的工作流系统提供了有力的事务处理技术支持。     

基于工作流技术的动态GIS服务链研究

提出了一种基于工作流技术的动态GIS服务链的实现方法,该方法使用抽象GIS服务组合服务链,由GIS服务动态选择机制将抽象服务映射到具体服务,并结合工作流技术,将工作流描述语言描述的GIS服务链流程送入工作流引擎,由工作流引擎管理和监控服务链的运行。     

数字土地信息系统的集成模式探讨

简述土地管理的特点,分析土地信息系统的组成,具体介绍如何实现数字土地信息系统的集成。     

面向数字化测绘生产工作流的Web存储系统

讨论了一个面向工作流的项目管理系统中 ,各类计划、实施、监督管理、汇总报表等结构化文档及非结构化文档的存储访问系统的构造方法 ,并设计与实现了一个数字化测绘生产管理系统实例     

An asynchronous Geoprocessing Workflow and its application to an Antarctic ozone monitoring and mapping service

The integration of Sensor Web Enablement services with other Open Geospatial Consortium (OGC) Web Services as Geospatial Processing Workflows (GPW) is essential for future Sensor Web application scenarios. With the help of GPW technology, distributed and heterogeneous OGC Web Services can be organized and integrated as compound Web Service applications that can direct complicated earth observation tasks. Under the Sensor Web environment, asynchronous communications between Sensor Web Services are common. We have proposed an asynchronous GPW architecture for the integration of Sensor Web Services into a Web Service Business Process Execution Language workflow technology. We designed a Sensor Information Accessing and Processing workflow, an asynchronous GPW instance, to take an experiment of observing and mapping ozone over Antarctica. Based on our results, our proposed asynchronous workflow method shows the advantages of taking environmental monitoring and mapping tasks.     

面向旅游规划的空间信息服务工作流构建

针对现有工作对规划支持不足的问题,该文通过集成空间信息服务、语义网络、Web服务发现与组合等技术,探索并建立了一套完整、有效,能充分提供旅游规划决策支持的空间信息服务工作流构建方法。阐述了面向旅游规划的空间信息服务工作流构建的关键技术,包括基于旅游规划本体库的空间信息服务发现方法,旅游规划语义驱动的空间信息服务组合算法,以及可靠的空间信息服务工作流过程调用机制。在此基础上开展实证研究,实现面向旅游规划的空间信息服务工作流原型系统,并对工作流构建关键方法的可行性进行了验证。     

Mapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow

An unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.     

Big Earth data: disruptive changes in Earth observation data management and analysis?

ABSTRACT

Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term ‘big Earth data’ emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.