Crowdsourcing geospatial data

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.     

Automatic search of geospatial features for disaster and emergency management

Although the fast development of OGC (Open Geospatial Consortium) WFS (Web Feature Service) technologies has undoubtedly improved the sharing and synchronization of feature-level geospatial information across diverse resources, literature shows that there are still apparent limitations in the current implementation of OGC WFSs. Currently, the implementation of OGC WFSs only emphasizes syntactic data interoperability via standard interfaces and cannot resolve semantic heterogeneity problems in geospatial data sharing. To help emergency responders and disaster managers find new ways of efficiently searching for needed geospatial information at the feature level, this paper aims to propose a framework for automatic search of geospatial features using Geospatial Semantic Web technologies and natural language interfaces. We focus on two major tasks: (1) intelligent geospatial feature retrieval using Geospatial Semantic Web technologies; (2) a natural language interface to a geospatial knowledge base and web feature services over the Semantic Web. Based on the proposed framework we implemented a prototype. Results show that it is practical to directly discover desirable geospatial features from multiple semantically heterogeneous sources using Geospatial Semantic Web technologies and natural language interfaces.     

地理信息服务网络与协同研究进展EI北大核心CSCD

互联网与大数据技术的高速发展为地理信息服务的互联共享与广泛应用提供了技术基础。面对资源海量、多源异构的地理信息服务,如何实现服务的有效组织并提供合理高效的服务组合,拓展地理信息服务的应用范围,满足更高层次的应用需求受到研究者的广泛关注。构建地理信息服务网络并通过语义实现服务协同,是一种可能的解决方案。本文分析了当前地理信息服务和地理信息服务网络研究的现状,依据服务网络领域的研究成果,从服务网络的表达和优化、服务协同的构建和优化两个方面探讨了基于网络构建的地理信息服务协同方法的发展潜力,进而提出了地理信息服务网络及协同面临的挑战与研究方向。     

空间服务语义模式的地理信息服务发现

如何从大规模地理信息服务集合中快速且准确地发现目标服务是地理信息服务应用中的一个关键问题。当前基于关键字的服务发现方式缺乏语义支持,搜索效率低。本文在WSMO/WSML框架下,提出了一种基于空间服务语义模式的服务发现方法,将地理信息从语法模式转换为语义模式,明确表达空间数据中隐含的知识,有效克服数据源之间的语义异构。该方法能够显著提高地理信息服务发现的查全率和查准率。     

Towards a framework for geospatial tangible user interfaces in collaborative urban planning

The increasing complexity of urban planning projects today requires new approaches to better integrate stakeholders with different professional backgrounds throughout a city. Traditional tools used in urban planning are designed for experts and offer little opportunity for participation and collaborative design. This paper introduces the concept of geospatial tangible user interfaces (GTUI) and reports on the design and implementation as well as the usability of such a GTUI to support stakeholder participation in collaborative urban planning. The proposed system uses physical objects to interact with large digital maps and geospatial data projected onto a tabletop. It is implemented using a PostGIS database, a web map server providing OGC web services, the computer vision framework reacTIVision, a Java-based TUIO client, and GeoTools. We describe how a GTUI has be instantiated and evaluated within the scope of two case studies related to real world collaborative urban planning scenarios. Our results confirm the feasibility of our proposed GTUI solutions to (a) instantiate different urban planning scenarios, (b) support collaboration, and (c) ensure an acceptable usability.     

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.     

Terra Populus’ architecture for integrated big geospatial services

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.     

A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences

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.     

基于开放互操作标准的分布式地理空间模型共享研究

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。     

The framework of a geospatial semantic web-based spatial decision support system for Digital Earth

Abstract

While significant progress has been made to implement the Digital Earth vision, current implementation only makes it easy to integrate and share spatial data from distributed sources and has limited capabilities to integrate data and models for simulating social and physical processes. To achieve effectiveness of decision-making using Digital Earth for understanding the Earth and its systems, new infrastructures that provide capabilities of computational simulation are needed. This paper proposed a framework of geospatial semantic web-based interoperable spatial decision support systems (SDSSs) to expand capabilities of the currently implemented infrastructure of Digital Earth. Main technologies applied in the framework such as heterogeneous ontology integration, ontology-based catalog service, and web service composition were introduced. We proposed a partition-refinement algorithm for ontology matching and integration, and an algorithm for web service discovery and composition. The proposed interoperable SDSS enables decision-makers to reuse and integrate geospatial data and geoprocessing resources from heterogeneous sources across the Internet. Based on the proposed framework, a prototype to assist in protective boundary delimitation for Lunan Stone Forest conservation was implemented to demonstrate how ontology-based web services and the services-oriented architecture can contribute to the development of interoperable SDSSs in support of Digital Earth for decision-making.     

Building geospatial infrastructure

ABSTRACT

Many visions for geospatial technology have been advanced over the past half century. Initially researchers saw the handling of geospatial data as the major problem to be overcome. The vision of geographic information systems arose as an early international consensus. Later visions included spatial data infrastructure, Digital Earth, and a nervous system for the planet. With accelerating advances in information technology, a new vision is needed that reflects today’s focus on open and multimodal access, sharing, engagement, the Web, Big Data, artificial intelligence, and data science. We elaborate on the concept of geospatial infrastructure, and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.     

Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data

ABSTRACT

Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models. The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity. Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side. Four practical examples are presented from the marine, climate, planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow. Web service technologies offer a time- and cost-effective way to access multi-dimensional data in a user-tailored format and allow for rapid application development or time-series extraction. Data transport is minimised and enhanced processing capabilities are offered. More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces. At the same time, data users have to become aware of the advantages of web services and be trained how to benefit from them most.     

基于多协议的地理信息服务集成

设计了多协议地理信息服务集成框架,探讨解决了其中的关键问题,实现了从不同地理信息服务获取的影像数据、矢量数据和DEM数据的无缝集成。     

MapSafe: A complete tool for achieving geospatial data sovereignty

Sensitive geographic data are invaluable assets for the people to whom they belong and their disclosure should be decided by the sovereign data owner (SDO). Due to several high-profile data breaches and business models that commercialize user data, the need for new approaches to geoprivacy and data sovereignty has grown. We propose MapSafe, a client web application that first obfuscates datasets using donut masking or hexagonal binning, separately, and thereafter implements a multi-level encryption scheme that permits SDOs to share the final encrypted volume containing the geospatial information when they choose and at a level of detail which they are comfortable. The authenticity verification of the volume is facilitated by storing the hash value corresponding to the encrypted volume immutably on the Blockchain as a public record. Our approach places geoprivacy under data guardians'’ control, and its integration capabilities promote its adoption in existing and future geospatial web systems.     

FedIDS: a federated cloud storage architecture and satellite image delivery service for building dependable geospatial platforms

Earth observation satellites produce large amounts of images/data that not only must be processed and preserved in reliable geospatial platforms but also efficiently disseminated among partners/researchers for creating derivative products through collaborative workflows. Organizations can face up this challenge in a cost-effective manner by using cloud services. However, outages and violations of integrity/confidentiality associated to this technology could arise. This article presents FedIDS, a suite of cloud-based components for building dependable geospatial platforms. The Fed component enables organizations to build shared geospatial data infrastructure through federation of independent cloud resources to withstand outages, whereas IDS avoids violations of integrity/confidentiality of images/data in sharing information and collaboration workflows. A FedIDS prototype, deployed in Spain and Mexico, was evaluated through a study case based on a satellite imagery captured by a Mexican antenna and another based on a satellite imagery of a European observation mission. The acquisition, storage and sharing of images among users of the federation, the exchange of images between Mexican and Spanish sites and outage scenarios were evaluated. The evaluation revealed the feasibility, reliability and efficiency of FedIDS, in comparison with available solutions, in terms of performance, storage consume and integrity/confidentiality when sharing images/data in collaborative scenarios.     

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.     

Expeditious management plan towards digital earth

The breakthrough developments in geospatial technologies and the increasing availability of spatial data make geoinformation a business and a decisional element to the management. Hence, it is important to have a management plan to factor in practical and feasible data sources, in building geo applications. The authors of this paper are motivated by the fact that right data sources could outclass in-house resources in various application scenarios. This paper outlines pragmatic cases for the tangible benefits of the existing potential data and expeditious patterns for digital earth. This work also proposes ‘good-enough’ solutions based on the pragmatic cases, available literature, and the 3D city model developed that could be sufficient in contriving the objectives of the common public usage and open business models. To demonstrate this approach, the paper encapsulated the low-cost development of virtual 3D city model using publicly available cadastral data and web services.     

多源地理空间矢量数据关联分析

针对多源地理空间矢量数据多来源、难以集成综合利用这一现状，本文提出了多源地理空间矢量数据关联方法，并以此为基础构建了多源地理空间矢量数据关联的可视与计算查询系统。首先，对多源地理空间矢量数据关联的概念及分类进行了定义。然后，以此为基础，提出了关联关系构建技术：自适应四叉树编码技术、扫描线技术、几何匹配及语义匹配技术。最后，为实现关联关系的直观展示，设计了原型系统。关联技术的提出可建立起多源地理空间矢量数据之间的关联关系，原型系统的构建也为用户综合利用多源地理空间矢量数据提供了平台，提高了数据的利用率及数据查询的效能。     

基于Petri网的地理信息服务组合模型研究

地理信息服务应用中,单一的功能服务往往无法独立完成复杂的地理信息应用,需要不同粒度的服务组合及协调机制。本文重点研究地理信息服务的组合建模方法,提出基于Petri网的地理信息服务组合建模理论。首先,给出基于Petri网的地理信息服务的定义和图形化描述,然后结合三维地理信息的应用,给出了三维空间分析服务组合的实例分析,最后通过烟草行业的地理信息服务应用验证基于Petri网的服务组合模型的可行性。     

Fundamental aspects of access control for geospatial data

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.