Geospatial information infrastructures to address spatial needs in health: Collaboration,challenges and opportunities

Most health-related issues such as public health outbreaks and epidemiological threats are better understood from a spatial–temporal perspective and, clearly demand related geospatial datasets and services so that decision makers may jointly make informed decisions and coordinate response plans. Although current health applications support a kind of geospatial features, these are still disconnected from the wide range of geospatial services and datasets that geospatial information infrastructures may bring into health. In this paper we are questioning the hypothesis whether geospatial information infrastructures, in terms of standards-based geospatial services, technologies, and data models as operational assets already in place, can be exploited by health applications for which the geospatial dimension is of great importance. This may be certainly addressed by defining better collaboration strategies to uncover and promote geospatial assets to the health community. We discuss the value of collaboration, as well as the opportunities that geographic information infrastructures offer to address geospatial challenges in health applications.     

Semantic Web-based geospatial knowledge transformation

Earth and space science research and applications typically involve collecting and analyzing large volumes of geospatial data much of which is derived from other existing data by applying a scientific workflow. Such a step-by-step process can be viewed as a process of geospatial knowledge transformation, which often involves hypotheses, inferences and integrations to derive user-specific data products from the knowledge of domain experts. Our research is focused on reducing the transformation effort by providing component inference and integration tools. The Semantic Web envisions a new standardized information infrastructure to enable interoperable machine-to-machine interactions and automatic or semi-automatic service chaining for deriving knowledge over networks. This paper describes a generic framework and implementation of how the Semantic Web proceeds through the life cycle of geospatial knowledge transformation, from geospatial modeling (knowledge formalization), through model instantiation (service chain) to model execution (data product). Our approach relies on semantic integrations. A number of ontologies used to capture domain knowledge are introduced in this paper as the basis of knowledge bases for describing and reasoning geospatial data and services. Also, a semantically enabled geospatial catalog service is described to enable more effective discovery, automation and integration of geospatial data and services.     

基于空间和属性数据的联合索引技术

传统联合查询通常需要空间数据库对2个索引进行检索,通过集合运算得到最终结果,运算效率较低。针对该缺陷,提出基于空间和属性数据的联合索引技术,在索引目录项中加入属性数据提高联合检索速度。实验结果表明,该技术对于属性与空间位置有较高相关度的数据,具有较高查询效率。     

Sharing geospatial provenance in a service-oriented environment

One of the earliest investigations of provenance was inspired by applications in GIS in the early 1990’s. Provenance records the processing history of a data product. It provides an information context to help users determine the reliability of data products. Conventional provenance applications in GIS focus on provenance capture, representation, and usage in a stand-alone environment such as a desktop-based GIS software system. They cannot support wide sharing and open access of provenance in a distributed environment. The growth of service-oriented sharing and processing of geospatial data brings some new challenges in provenance-aware applications. One is how to share geospatial provenance in an interoperable way. This paper describes the development of provenance service for geospatial data products using the ebXML Registry Information Model (ebRIM) of a geospatial catalog service, which follows the interface specifications of the OGC Catalogue Services for the Web (CSW). This approach fits well the current service stack of the GIS domain and facilitates the management of geospatial data provenance in an open and distributed environment.     

GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval

It is still a great challenge to efficiently deliver dynamic and heterogeneous Earth observation (EO) information to users based on their real time locations. However, the rapidly evolving techniques create a chance to meet the challenge. This paper proposes a framework to realize a one-stop and location based service (LBS) for geospatial information (GI) retrieval on mobile platforms. The framework originally integrates a number of state-of-the-art techniques with geospatial data resources and let them cooperate together to provide a robust and highly available LBS. Cloud platform is used to deploy the server module. A location enabled load balancing algorithm is presented to balance the cloud instance VMs on behalf of LBS. A system named GeoFairy is implemented. It provides a one-stop service for gathering and delivering twelve kinds of GI on real time locations. Two Apps are built for the major mobile ecosystems: iOS and Android. Many tests, including a stress test, have been made via a number of mobile devices at various locations. The results demonstrate that GeoFairy is capable of one-stop delivering real-time GI to users and significantly reducing costs on information searching and retrieving. This feature is very helpful in many scenarios such as disaster responding and military actions. This research paves a way on both theoretical and practical aspects for researchers and developers to realize operational mobile applications for one stop and location based GI retrieval.     

联合科学数据中心的在线水文模型服务研究

在线水文或陆面过程模型服务可以为广泛的网络用户提供数据分析、预报预警和决策支持等功能。随着科学数据共享环境发展的日趋成熟,如何便捷地利用丰富的在线数据资源成为在线模型的新挑战。当前的在线模型服务研究对于复杂多样的模型数据和参数需求的标准化不足,且缺少与科学数据中心的紧密协作。建立了一个模型服务器、数据服务器、客户端三方紧密协作的在线模型服务方案。结合一般性工作流程,基于标准化的服务接口来构建在线的水文和陆面过程模型和数据服务,重点介绍了在线服务架构、主要接口及实现的关键技术,包括模型数据和参数的传递和格式转换、数据服务器数据查询的实现和模拟结果的返回。这套方案充分利用了模型服务器的计算优势和科学数据中心的资源,可以被复制和扩展到其他类似应用。     

可视化城市级停车场服务能力动态覆盖模型

覆盖模型通常用于评估空间网络站点规划及网络健壮性,而可视化的覆盖模型可以快速清 晰地传递信息,有助于用户理解和分析相关空间数据。该研究基于城市级停车场服务能力空间网络模 型,提出了一种可视的动态覆盖方法,其通过 ParkingRank 算法对各停车场的服务能力进行动态量化, 采用具有边界约束的改进加权 Voronoi 图,将各停车场的量化值映射到地理空间,实现了对整个城市 区域无缝且不重叠的覆盖。该模型最大的优点是每个停车场的覆盖范围可随其量化值的变化而动态调 整,实现了停车场网络模型的动态可视化,可为未来城市级停车管理与服务提供强大的决策支持。     

基于超地图原理的分布式空间数据模型

分而式、部件化、交互性与互操作是因特网地理信息系统的4个主要特征，为了实现对空间信息的分布式管理与处理，以便将大容量、多源地理信息纳入统一的框架管理，基于超地图与分布式数据库的原理，提出了满足上述特征的因特网地理信息系统分布式地理信息空间数据模型，并以目录服务作为空间信息全球化的范式，同时定义了不同层次超链节点的目录服务数据结构，以便通过超链接实现分布式空间数据模型的关联，该模型已在Geosurf中得以应用，并取得了良好效果。     

农业空间信息资源共享服务平台

针对农业空间信息资源的共享和服务中跨部门、多数据源和数据异质性问题,提出运用OGC WMS规范来建立基于Web Services的农业空间信息资源共享服务平台,解决农业空间信息资源的异质共享问题。给出了农业空间信息资源共享与服务平台的框架、功能和具体的实现策略,可为同类应用提供借鉴。     

A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval

A critical issue of large-scale multimedia retrieval is how to develop an effective framework for ranking the search results. This problem is particularly challenging for content-based video retrieval due to some issues such as short text queries, insufficient sample learning, fusion of multimodal contents, and large-scale learning with huge media data. In this paper, we propose a novel multimodal and multilevel (MMML) ranking framework to attack the challenging ranking problem of content-based video retrieval. We represent the video retrieval task by graphs and suggest a graph based semi-supervised ranking (SSR) scheme, which can learn with small samples effectively and integrate multimodal resources for ranking smoothly. To make the semi-supervised ranking solution practical for large-scale retrieval tasks, we propose a multilevel ranking framework that unifies several different ranking approaches in a cascade fashion. We have conducted empirical evaluations of our proposed solution for automatic search tasks on the benchmark testbed of TRECVID2005. The promising empirical results show that our ranking solutions are effective and very competitive with the state-of-the-art solutions in the TRECVID evaluations.     

Geometric modeling of geospatial data for visualization-assisted excavation

Underground utility lines being struck by mechanized excavators during construction or maintenance operations is a long standing problem. Besides the disruptions to public services, daily life, and commerce, utility strike accidents lead to injuries, fatalities, and property damages that cause significant financial loss. Utility strikes by excavation occur mainly because of the lack of an effective approach to synergize the geospatial utility locations and the movement of excavation equipment into a real-time, three-dimensional (3D) spatial context that is accessible to excavator operators. A critical aspect of enabling such a knowledge-based excavation approach is the geospatial utility data and its geometric modeling. Inaccurate and/or incomplete utility location information could lead to false instilled confidence and be counterproductive to the excavator operator. This paper addresses the computational details in geometric modeling of geospatial utility data for 3D visualization and proximity monitoring to support knowledge-based excavation. The details of the various stages in the life-cycle of underground utility geospatial data are described, and the inherent limitations that preclude the effective use of the data in downstream engineering applications such as excavation guidance are analyzed. Five key requirements - Interactivity, Information Richness, 3-Dimensionality, Accuracy Characterization, and Extensibility – are identified as necessary for the consumption of geospatial utility data in location-sensitive engineering applications. A visualization framework named IDEAL that meets the outlined requirements is developed and presented in this paper to geometrically represent buried utility geospatial data and the movement of excavation equipment in a 3D emulated environment in real-time.     

Feature extraction of hyperspectral images with a matting model

Owing to the limitations of the imaging sensor and theoretical aspect, hyperspectral images (HSIs) are contaminated with some unwanted components such as noise and a lack of spatial information. This article proposes a spatial–spectral feature enhancement model to eliminate interference, modify spectral distortion, and increase the useful features. The framework firstly proposes an effective spatial feature-based strategy for selecting a band with the most edge information to serve as alpha channel. Given the alpha channel, the continuous foreground and background are estimated by the closed form solution. Finally, feature-enhanced HSI is obtained by linearly combining the selected band, hyper foreground and background. Experimental results of the ground-based data and remotely sensed data indicate that the proposed feature enhancement algorithm provides effective performance in enhancing spatial–spectral features and reducing noise. Especially, the feature-enhanced data have positive influence on both unmixing and classification.     

THREDDS与CS/W地理空间目录服务的互操作研究

CS/W是开放式地理信息协会（OGC）所制定的地理空间信息目录服务规范,它被广泛应用于空间信息,土地科学等研究领域之中。THREDDS是由UNIDATA开发的空间数据发布查询网络服务系统,其元数据目录服务范围集中于气象、海洋等领域。当前地理信息数据共享与互操作日益重要,然而CS/W和THREDDS之间的这种跨公共团体、跨协议的地理信息互操作研究成果还十分缺乏。针对两者协议之间所存在的互操作障碍进行分析,对比研究了OGC地理空间与气象海洋两个公共团体之间的目录元数据服务CS/W和THREDDS的差异性,提出了信息模型转换的方法与思路,进行了元数据的语义映射,并在此基础之上设计开发了一款连接两者的中间件Ingestor系统,从而有效的增强两者之间的互操作性,促进了跨协议、跨公共团体的目录服务之间的共享。     

Use of ebRIM-based CSW with sensor observation services for registry and discovery of remote-sensing observations

Recent advances in Sensor Web geospatial data capture, such as high-resolution in satellite imagery and Web-ready data processing and modeling technologies, have led to the generation of large numbers of datasets from real-time or near real-time observations and measurements. Finding which sensor or data complies with criteria such as specific times, locations, and scales has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. In this paper, an architecture for use of the integration Sensor Observation Service (SOS) with the Open Geospatial Consortium (OGC) Catalogue Service-Web profile (CSW) is put forward. The architecture consists of a distributed geospatial sensor observation service, a geospatial catalogue service based on the ebXML Registry Information Model (ebRIM), SOS search and registry middleware, and a geospatial sensor portal. The SOS search and registry middleware finds the potential SOS, generating data granule information and inserting the records into CSW. The contents and sequence of the services, the available observations, and the metadata of the observations registry are described. A prototype system is designed and implemented using the service middleware technology and a standard interface and protocol. The feasibility and the response time of registry and retrieval of observations are evaluated using a realistic Earth Observing-1 (EO-1) SOS scenario. Extracting information from SOS requires the same execution time as record generation for CSW. The average data retrieval response time in SOS+CSW mode is 17.6% of that of the SOS-alone mode. The proposed architecture has the more advantages of SOS search and observation data retrieval than the existing sensor Web enabled systems.     

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.     

MEMORY ORGANIZATION AS THE MISSING LINK BETWEEN CASE-BASED REASONING AND INFORMATION RETRIEVAL IN BIOMEDICINE

Mémoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retrieval (IR). IR systems have been able to scale up to terabytes of data taking advantage of large databases research to build Internet search engines. They search for pertinent documents to answer a query using term-based ranking and/or global ranking schemes. Similarly, case-based reasoning (CBR) systems search for pertinent cases using a scoring function for ranking the cases. Mémoire proposes a memory organization based on inverted indexes which may be powered by databases to search and rank efficiently through large case bases. It can be seen as a first step toward large-scale CBR systems, and in addition provides a framework for tight cooperation between CBR and IR.