Structural Alignment Methods with Applications to Geospatial Ontologies

We consider the problem of enabling interoperability and information sharing among geospatial applications that use ontologies to describe their concepts and the relationships among them. We present two fully automatic alignment methods that use the graph structures of a pair of ontologies to establish their alignment, that is, the semantic correspondences between their concepts. We have tested our methods on geospatial ontologies pertaining to wetlands and four other pairs that belong to a repository that has been used in the Ontology Alignment Evaluation Initiative (OAEI). Using these ontologies, we have compared the effectiveness (precision and recall) of our methods against the Similarity Flooding Algorithm that was proposed by others and show that for each of the tested ontologies one of our methods is at least as effective as their method. We have tuned the performance of our methods by introducing a greedy approach that reduces the number of concepts that get compared. This approach reduces runtime by approximately 30% with a minor compromise to the effectiveness of the results. To further validate our approach, we participated in the OAEI competition to align a pair of ontologies, each with a few thousand concepts.     

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

ABSTRACT

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data. However, data interlinking, which is the most valuable contribution of Linked Data, remains incomplete and inaccurate. This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain. According to the characteristics and roles of geospatial data in data discovery, eight elementary data characteristics are adopted as data interlinking types. These elementary characteristics are further combined to form compound and overall data interlinking types. Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively. Therefore, geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value. The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data. The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network (NSTI-GEO) and data -links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.     

Towards intelligent geospatial data discovery: a machine learning framework for search ranking

Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset. This study reports a machine learning framework to address the ranking challenge, the fundamental obstacle in geospatial data discovery, by (1) identifying a number of ranking features of geospatial data to represent users’ multidimensional preferences by considering semantics, user behavior, spatial similarity, and static dataset metadata attributes; (2) applying a machine learning method to automatically learn a ranking function; and (3) proposing a system architecture to combine existing search-oriented open source software, semantic knowledge base, ranking feature extraction, and machine learning algorithm. Results show that the machine learning approach outperforms other methods, in terms of both precision at K and normalized discounted cumulative gain. As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain, we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.     

Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey

Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.     

Spatio-temporal evaluation matrices for geospatial data

The global geospatial community is investing substantial effort in providing tools for geospatial data-quality information analysis and systematizing the criteria for geospatial data quality. The importance of these activities is increasing, especially in the last decade, which has witnessed an enormous expansion of geospatial data use in general and especially among mass users. Although geospatial data producers are striving to define and present data-quality standards to users and users increasingly need to assess the fitness for use of the data, the success of these activities is still far from what is expected or required. As a consequence, neglect or misunderstanding of data quality among users results in misuse or risks. This paper presents an aid in spatio-temporal quality evaluation through the use of spatio-temporal evaluation matrices (STEM) and the index of spatio-temporal anticipations (INSTANT) matrices. With the help of these two simple tools, geospatial data producers can systematically categorize and visualize the granularity of their spatio-temporal data, and users can present their requirements in the same way using business intelligence principles and a Web 2.0 approach. The basic principles and some examples are presented in the paper, and potential further applied research activities are briefly described.     

A case-based reasoning approach for task-driven spatial–temporally aware geospatial data discovery through geoportals

ABSTRACT

There is a critical need to develop a means for fast, task-driven discovery of geospatial data found in geoportals. Existing geoportals, however, only provide metadata-based means for discovery, with little support for task-driven discovery, especially when considering spatial–temporal awareness. To address this gap, this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery (CBR-GDD) method and implementation that accesses geospatial data by tasks. The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness, thus providing solutions based on past tasks. The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals: ontology-enhanced knowledge base, similarity assessment model, and case retrieval nets. A set of experiments and case studies validate the CBR-GDD approach and application, and demonstrate its efficiency.     

Developing a geospatial web-GIS system for landscape and urban planning

This study discusses a geographical information system (GIS) for operating a local government's landscape and urban planning activities via a website. Implementing this web-GIS system will help build a more realistic landscape and urban planning model that includes citizen participation and city marketing. The approach is applicable to ubiquitous city (u-city) development based on geospatial web and its related systems. The approach presented is built on six selected elements of a u-city system. The outcome of the study includes sustainable analysis, environmental planning, urban planning, and city marketing. The outcome is applicable to cities that are planning to adopt the u-city system or advanced telecommunication or planning tools into their urban frameworks.     

展望大数据时代的地球空间信息学

20世纪90年代,随着全球信息化和互联网的推进,地球空间信息学应运而生,推动了数字地球和数字城市的建设。21世纪以来,随着全球信息化与工业化的高度集成发展,出现了物联网和云计算,人类进入了大数据时代。本文论述大数据时代地球空间信息学的特点(无所不在、多维动态、互联网+网络化、全自动与实时化、从感知到认知、众包与自发地理信息、面向服务)和必须解决的主要关键技术问题(全球空天地一体化的非线性地球参考框架构建技术、星基导航增强技术、天地一体化网络通信技术、多源成像数据在轨处理技术、天基信息智能终端服务技术、天基资源调度与网络安全、基于载荷的多功能卫星平台设计与研制)。本文最后给出大数据时代地球空间信息学的新定义,即地球空间信息学是用各种手段和集成各种方法对地球及地球上的实体目标(physical objects)和人类活动(human activities)进行时空数据采集、信息提取、网络管理、知识发现、空间感知认知和智能位置服务的一门多学科交叉的科学和技术。从这个新定义出发,地球空间信息学将在构建智慧地球和智慧城市的大数据时代面临更多的发展机遇和艰巨的任务,必将为人类社会的进步和可持续发展作出更大的贡献。     

GeoCube： 面向大规模分析的多源对地观测时空立方体

随着对地立体观测体系的建立,遥感大数据不断累积。传统基于文件、景/幅式的影像组织方式,时空基准不够统一,集中式存储不利于大规模并行分析。对地观测大数据分析仍缺乏一套统一的数据模型与基础设施理论。近年来,数据立方体的研究为对地观测领域大数据分析基础设施提供了前景。基于统一的分析就绪型多维数据模型和集成对地观测数据分析功能,可构建一个基于数据立方的对地观测大数据分析基础设施。因此,本文提出了一个面向大规模分析的多源对地观测时空立方体,相较于现有的数据立方体方法,强调多源数据的统一组织、基于云计算的立方体处理模式以及基于人工智能优化的立方体计算。研究有助于构建时空大数据分析的新框架,同时建立与商业智能领域的数据立方体关联,为时空大数据建立统一的时空组织模型,支持大范围、长时序的快速大规模对地观测数据分析。本文在性能上与开源数据立方做了对比,结果证明提出的多源对地观测时空立方体在处理性能上具有明显优势。     

面向地理空间数据更新的数据同化

针对目前地理空间数据整合与持续高效更新存在的问题,将数据同化这一概念和思想引入地理空间数据的更新,目的是提高空间数据质量和空间数据库更新的效率。着重论述了地理空间数据同化的概念、内涵和关键技术。最后提出了地理事件-条件-操作GECA(Geographical Event-Condition-Action)规则驱动下的基于数据同化的地理空间数据更新机制。     

数字地球建设中的地理空间Metadata研究

为解决数字地球建设中现有海量地理空间信息的共建共享,有效管理和利用,结合国际地理空间Metadata标准规范,对数字地球建设中所需的地理空间Metadata的定义,作用,分类,内容,实现模型与实例及其应用进行详细研究和设计,提出用于数字地球建设的地理空间Metadata网络管理系统解决方案,并设计实现了原型系统,为数字地球建设中现有海量地理空间信息的有效管理和共建共享提供了理论基础和切实可行的技术支撑。对国家空间信息基础设施建设和数字地球建设具有重要的推动作用和深远的社会意义。     

AR技术在GIS可视化中的应用及方法研究

地理数据的增强现实可视化技术,是地理信息科学可视化的一个重要的发展方向之一。首先,笔者详细地综述了国内外AR技术在GIS可视化领域的应用。针对基于真实视频影像表达地理数据的问题,笔者构建了该可视化系统的概念模型;并以户外山区地形数据和影像数据为基础,详细地阐述了AR技术可视化地理数据的原理和方法;最后,实现了以视频影像为载体的地理数据AR可视化方法。与传统地理数据的可视化方式相比,AR可视化表达方式更简单直观,真实感更强。     

A Rule-Based Strategy for the Semantic Annotation of Geodata

The ability to represent geospatial semantics is of great importance when building geospatial applications for the Web. This ability will enhance discovery, retrieval and translation of geographic information as well as the reuse of geographic information in different contexts. The problem of generating semantic annotations has been recognized as one of the most serious obstacles for realizing the Geospatial Semantic Web vision. We present a rule‐based strategy for the semantic annotation of geodata that combines Semantic Web and Geospatial Web Services technology. In our approach, rules are employed to partially automate the annotation process. Rules define conditions for identifying geospatial concepts. Based on these rules, spatial analysis procedures are implemented that allow for inferring whether or not a feature in a dataset represents an instance of a geospatial concept. This automated evaluation of features in the dataset generates valuable information for the creation and refinement of semantic annotations on the concept level. The approach is illustrated by a case study on annotating data sources containing representations of lowlands. The presented strategy lays the foundations for the specification of a semantic annotation tool for geospatial web services that supports data providers in annotating their sources according to multiple domain views.     

Temporal divergence in cropping pattern and its implications on geospatial drought assessment

Time series data on cropping pattern at disaggregated level were analysed and its implications on geospatial drought assessment were demonstrated. An index of Cropping Pattern Dissimilarity (CP-DI) between a pair of years, developed in this study, proved that the cropping pattern of a year has a higher degree of similarity with that of recent past years only and tends to be dissimilar with longer time difference. The temporal divergence in cropping pattern has direct implications on geospatial approach of drought assessment, in which, time series NDVI data are compared for drought interpretation. It was found that, seasonal NDVI profiles of drought year and normal year did not show any anomaly when the cropping patterns were dissimilar and two normal years having dissimilar cropping pattern showed different NDVI profiles. Therefore, it is suggested that such temporal comparisons of NDVI are better restricted to recent past years to achieve more objective interpretation.     

Data democracy – increased supply of geospatial information and expanded participatory processes in the production of data

The global landscape in the supply, co-creation and use of geospatial data is changing very rapidly with new satellites, sensors and mobile devices reconfiguring the traditional lines of demand and supply and the number of actors involved. In this paper we chart some of these technology-led developments and then focus on the opportunities they have created for the increased participation of the public in generating and contributing information for a wide range of uses, scientific and non. Not all this information is open or geospatial, but sufficiently large portions of it are to make it one of the most significant phenomena of the last decade. In fact, we argue that while satellite and sensors have exponentially increased the volumes of geospatial information available, the participation of the public is transformative because it expands the range of participants and stakeholders in society using and producing geospatial information, with opportunities for more direct participation in science, politics and social action.     

Visualization and level-of-detail of metadata for interactive exploration of Sensor Web

There are several issues with Web-based search interfaces on a Sensor Web data infrastructure. It can be difficult to (1) find the proper keywords for the formulation of queries and (2) explore the information if the user does not have previous knowledge about the particular sensor systems providing the information. We investigate how the visualization of sensor resources on a 3D Web-based Digital Earth globe organized by level-of-detail (LOD) can enhance search and exploration of information by easing the formulation of geospatial queries against the metadata of sensor systems. Our case study provides an approach inspired by geographical mashups in which freely available functionality and data are flexibly combined. We use PostgreSQL, PostGIS, PHP, and X3D-Earth technologies to allow the Web3D standard and its geospatial component to be used for visual exploration and LOD control of a dynamic scene. Our goal is to facilitate the dynamic exploration of the Sensor Web and to allow the user to seamlessly focus in on a particular sensor system from a set of registered sensor networks deployed across the globe. We present a prototype metadata exploration system featuring LOD for a multiscaled Sensor Web as a Digital Earth application.     

Hypercube-Based Visualization Architecture for Web-Based Environmental Geospatial Information Systems

The geospatial field significantly influences the development of the environmental domain, including a wide range of geospatial and cartographic information systems, such as Geographic Information Systems (GIS), Multimedia Atlas Information Systems (MAIS) and more recently web-based GIS (Web-GIS). In this context, we introduce web-based environmental geospatial information systems (Web-EGIS) as a special case of Web-GIS, aiming to integrate the functionalities of geospatial information systems with the enormous quantity of specialized, distributed and highly heterogeneous environmental geo-referenced data and services. We define three main characteristics of the Web-EGIS. The first is a generic hypercube-based data organization and visualization. The next characteristic is a standard-based, three-tier service-oriented architecture. The third and last characteristic is the traceability of the architectural and design decisions, for which we introduce the new concept of an ‘Environmental Application Context’, arguing that not only the functional but also the non-functional requirements (NFRs) have an important role in defining the architecture, software components and data services of such systems. In a nutshell, a Web-EGIS is characterized by a coherent user experience through the hypercube-based visualization concept, technically supported by a service-oriented architecture that is structured according to an extensive analysis of NFRs. On this basis, we introduce the Geodata visualization and interactive training environment (GeoVITe) Platform for Interdisciplinary Environmental Research as a reference implementation of a Web-EGIS with its basic design requirements, integrative hypercube-based visualization for heterogeneous data sources, extended web cartography functionalities, and its de facto system architecture. The concept of the hypercube-based visualization is better transferred to the system architecture by understanding the ‘Environmental Application Context’ of this particular Web-EGIS, which also makes it easier to maintain and enhance. The resulting system is a support platform for research activities in the environmental domain.     

Ontology‐supported Querying of Geographical Databases

Querying geographical information systems has been recognized as a difficult task for non‐expert users. Furthermore, user queries are often characterized by semantic aspects not directly managed by traditional spatial databases or GIS. Examples of such semantic geospatial queries are the use of implicit spatial relations between objects, or the reference of domain concepts not explicitly represented in data. To handle such queries, we envisage a system that translates natural language queries into spatial SQL statements on a database, thus improving standard GIS with new semantic capabilities. Within this general objective, the contribution of this article is to introduce a methodology to handle semantic geospatial queries issued over a spatial database. This approach captures semantics from an ontology built upon the spatial database and enriched by domain concepts and properties specifically defined to represent the localization of objects. Some examples of the use of the methodology in the urban domain are presented.     

互联网泛在地理信息感知融合技术综述

互联网数据中蕴含丰富的地理信息,其无处不在、形式与结构多样的特征决定了感知和融合面临许多技术难题。本文在分析互联网泛在地理信息分类和特征的基础上,系统研究其感知和融合技术的总体现状,总结了服务快速准确发现、深层网络数据高覆盖度采集、非结构化文本中位置信息提取和关联图像空间语义提取等感知技术发展现状,分析了异源几何数据匹配关联、地址标准化处理、同名实体语义对齐、地理实体关系构建等融合处理关键技术;在此基础上,总结和展望了互联网泛在地理信息感知融合技术在开放地理数据网络、城市治理与应急管理、网络监测与地理空间情报等领域的应用前景。     

Geospatial information service based on digital measurable image

LBS (location-based service) is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation Internet and Web 2.0, a new model of geospatial information service based on DMI (digital measurable image) is presented. First, the concept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City·Wuhan as an example, the function of geospatial information service based on DMI is introduced. Finally, the feasibility for its industrialization is discussed.