Interoperable Exchange Of Surface Solar Irradiance Observations: A Challenge

International audienceWe present how implementations of the Sensor Web Enablement framework of the Open Geospatial Consortium are integrated into an existing spatial data infrastructure. The result is registered as a community portal for professionals in solar energy in the GEOSS Common Infrastructure, demonstrating the benefits of interoperable exchange of in-situ time-series observations of surface solar irradiation. Easy access to, and sharing of data improves the information base for planning and monitoring of solar power resources. Providing users with visualization and download functionality for in-situ measurements is a key aspect for engaging the energy community to share, release and integrate in-situ measurements

Application of ESE Data and Tools to Air Quality Management: Services for Helping the Air Quality Community use ESE Data (SHAirED)

The goal of this REASoN applications and technology project is to deliver and use Earth Science Enterprise (ESE) data and tools in support of air quality management. Its scope falls within the domain of air quality management and aims to develop a federated air quality information sharing network that includes data from NASA, EPA, US States and others. Project goals were achieved through a access of satellite and ground observation data, web services information technology, interoperability standards, and air quality community collaboration. In contributing to a network of NASA ESE data in support of particulate air quality management, the project will develop access to distributed data, build Web infrastructure, and create tools for data processing and analysis. The key technologies used in the project include emerging web services for developing self describing and modular data access and processing tools, and service oriented architecture for chaining web services together to assemble customized air quality management applications. The technology and tools required for this project were developed within DataFed.net, a shared infrastructure that supports collaborative atmospheric data sharing and processing web services. Much of the collaboration was facilitated through community interactions through the Federation of Earth Science Information Partners (ESIP) Air Quality Workgroup. The main activities during the project that successfully advanced DataFed, enabled air quality applications and established community-oriented infrastructures were: develop access to distributed data (surface and satellite), build Web infrastructure to support data access, processing and analysis create tools for data processing and analysis foster air quality community collaboration and interoperability

Grid Approach to Satellite Monitoring Systems Integration

This paper highlights the challenges of satellite monitoring systems integration, in particular based on Grid platform, and reviews possible solutions for these problems. We describe integration issues on different levels: data integration level and task management level (job submission in terms of Grid). We show example of described technologies for integration of monitoring systems of Ukraine (National Space Agency of Ukraine, NASU) and Russia (Space Research Institute RAS, IKI RAN). Another example refers to the development of InterGrid infrastructure that integrates several regional and national Grid systems: Ukrainian Academician Grid (with Satellite data processing Grid segment) and RSGS Grid (Chinese Academy of Sciences)

Sensor Web Interoperability Testbed Results Incorporating Earth Observation Satellites

This paper describes an Earth Observation Sensor Web scenario based on the Open Geospatial Consortium s Sensor Web Enablement and Web Services interoperability standards. The scenario demonstrates the application of standards in describing, discovering, accessing and tasking satellites and groundbased sensor installations in a sequence of analysis activities that deliver information required by decision makers in response to national, regional or local emergencies

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains

On-line data archives

©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Digital libraries and other large archives of electronically retrievable and manipulable material are becoming widespread in both commercial and scientific arenas. Advances in networking technologies have led to a greater proliferation of wide-area distributed data warehousing with associated data management challenges. We review tools and technologies for supporting distributed on-line data archives and explain our key concept of active data archives, in which data can be, processed on-demand before delivery. We are developing wide-area data warehousing software infrastructure for geographically distributed archives of large scientific data sets, such as satellite image data, that are stored hierarchically on disk arrays and tape silos and are accessed by a variety of scientific and decision support applications. Interoperability is a major issue for distributed data archives and requires standards for server interfaces and metadata. We review present activities and our contributions in developing such standards for different application areas.K. Hawick, P. Coddington, H. James, C. Patte

Search improvement within the geospatial web in the context of spatial data infrastructures

El trabajo desarrollado en esta tesis doctoral demuestra que es posible mejorar la búsqueda en el contexto de las Infraestructuras de Datos Espaciales mediante la aplicación de técnicas y buenas prácticas de otras comunidades científicas, especialmente de las comunidades de la Web y de la Web Semántica (por ejemplo, Linked Data). El uso de las descripciones semánticas y las aproximaciones basadas en el contenido publicado por la comunidad geoespacial pueden ayudar en la búsqueda de información sobre los fenómenos geográficos, y en la búsqueda de recursos geoespaciales en general. El trabajo comienza con un análisis de una aproximación para mejorar la búsqueda de las entidades geoespaciales desde la perspectiva de geocodificación tradicional. La arquitectura de geocodificación compuesta propuesta en este trabajo asegura una mejora de los resultados de geocodificación gracias a la utilización de diferentes proveedores de información geográfica. En este enfoque, el uso de patrones estructurales de diseño y ontologías en esta aproximación permite una arquitectura avanzada en términos de extensibilidad, flexibilidad y adaptabilidad. Además, una arquitectura basada en la selección de servicio de geocodificación permite el desarrollo de una metodología de la georreferenciación de diversos tipos de información geográfica (por ejemplo, direcciones o puntos de interés). A continuación, se presentan dos aplicaciones representativas que requieren una caracterización semántica adicional de los recursos geoespaciales. El enfoque propuesto en este trabajo utiliza contenidos basados en heurísticas para el muestreo de un conjunto de recursos geopesaciales. La primera parte se dedica a la idea de la abstracción de un fenómeno geográfico de su definición espacial. La investigación muestra que las buenas prácticas de la Web Semántica se puede reutilizar en el ámbito de una Infraestructura de Datos Espaciales para describir los servicios geoespaciales estandarizados por Open Geospatial Consortium por medio de geoidentificadores (es decir, por medio de las entidades de una ontología geográfica). La segunda parte de este capítulo desglosa la aquitectura y componentes de un servicio de geoprocesamiento para la identificación automática de ortoimágenes ofrecidas a través de un servicio estándar de publicación de mapas (es decir, los servicios que siguen la especificación OGC Web Map Service). Como resultado de este trabajo se ha propuesto un método para la identificación de los mapas ofrecidos por un Web Map Service que son ortoimágenes. A continuación, el trabajo se dedica al análisis de cuestiones relacionadas con la creación de los metadatos de recursos de la Web en el contexto del dominio geográfico. Este trabajo propone una arquitectura para la generación automática de conocimiento geográfico de los recursos Web. Ha sido necesario desarrollar un método para la estimación de la cobertura geográfica de las páginas Web. Las heurísticas propuestas están basadas en el contenido publicado por os proveedores de información geográfica. El prototipo desarrollado es capaz de generar metadatos. El modelo generado contiene el conjunto mínimo recomendado de elementos requeridos por un catálogo que sigue especificación OGC Catalogue Service for the Web, el estandar recomendado por deiferentes Infraestructuras de Datos Espaciales (por ejemplo, the Infrastructure for Spatial Information in the European Community (INSPIRE)). Además, este estudio determina algunas características de la Web Geoespacial actual. En primer lugar, ofrece algunas características del mercado de los proveedores de los recursos Web de la información geográfica. Este estudio revela algunas prácticas de la comunidad geoespacial en la producción de metadatos de las páginas Web, en particular, la falta de metadatos geográficos. Todo lo anterior es la base del estudio de la cuestión del apoyo a los usuarios no expertos en la búsqueda de recursos de la Web Geoespacial. El motor de búsqueda dedicado a la Web Geoespacial propuesto en este trabajo es capaz de usar como base un motor de búsqueda existente. Por otro lado, da soporte a la búsqueda exploratoria de los recursos geoespaciales descubiertos en la Web. El experimento sobre la precisión y la recuperación ha demostrado que el prototipo desarrollado en este trabajo es al menos tan bueno como el motor de búsqueda remoto. Un estudio dedicado a la utilidad del sistema indica que incluso los no expertos pueden realizar una tarea de búsqueda con resultados satisfactorios

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

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

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

The Analysis of Open Source Software and Data for Establishment of GIS Services Throughout the Network in a Mapping Organization at National or International Level

Federal agencies and their partners collect and manage large amounts of geospatial data but it is often not easily found when needed, and sometimes data is collected or purchased multiple times. In short, the best government data is not always organized and managed efficiently to support decision making in a timely and cost effective manner. National mapping agencies, various Departments responsible for collection of different types of Geospatial data and their authorities cannot, for very long, continue to operate, as they did a few years ago like people living in an island. Leaders need to look at what is now possible that was not possible before, considering capabilities such as cloud computing, crowd sourced data collection, available Open source remotely sensed data and multi source information vital in decision-making as well as new Web-accessible services that provide, sometimes at no cost. Many of these services previously could be obtained only from local GIS experts. These authorities need to consider the available solution and gather information about new capabilities, reconsider agency missions and goals, review and revise policies, make budget and human resource for decisions, and evaluate new products, cloud services, and cloud service providers. To do so, we need, choosing the right tools to rich the above-mentioned goals. As we know, Data collection is the most cost effective part of the mapping and establishment of a Geographic Information system. However, it is not only because of the cost for the data collection task but also because of the damages caused by the delay and the time that takes to provide the user with proper information necessary for making decision from the field up to the user’s hand. In fact, the time consumption of a project for data collection, processing, and presentation of geospatial information has more effect on the cost of a bigger project such as disaster management, construction, city planning, environment, etc. Of course, with such a pre-assumption that we provide all the necessary information from the existing sources directed to user’s computer. The best description for a good GIS project optimization or improvement is finding a methodology to reduce the time and cost, and increase data and service quality (meaning; Accuracy, updateness, completeness, consistency, suitability, information content, integrity, integration capability, and fitness for use as well as user’s specific needs and conditions that must be addressed with a special attention). Every one of the above-mentioned issues must be addressed individually and at the same time, the whole solution must be provided in a global manner considering all the criteria. In this thesis at first, we will discuss about the problem we are facing and what is needed to be done as establishment of National Spatial Data Infra-Structure (NSDI), the definition and related components. Then after, we will be looking for available Open Source Software solutions to cover the whole process to manage; Data collection, Data base management system, data processing and finally data services and presentation. The first distinction among Software is whether they are, Open source and free or commercial and proprietary. It is important to note that in order to make distinction among softwares it is necessary to define a clear specification for this categorization. It is somehow very difficult to distinguish what software belongs to which class from legal point of view and therefore, makes it necessary to clarify what is meant by various terms. With reference to this concept there are 2 global distinctions then, inside each group, we distinguish another classification regarding their functionalities and applications they are made for in GIScience. According to the outcome of the second chapter, which is the technical process for selection of suitable and reliable software according to the characteristics of the users need and required components, we will come to next chapter. In chapter 3, we elaborate in to the details of the GeoNode software as our best candidate tools to take responsibilities of those issues stated before. In Chapter 4, we will discuss the existing Open Source Data globally available with the predefined data quality criteria (Such as theme, data content, scale, licensing, and coverage) according to the metadata statement inside the datasets by mean of bibliographic review, technical documentation and web search engines. We will discuss in chapter 5 further data quality concepts and consequently define sets of protocol for evaluation of all datasets according to the tasks that a mapping organization in general, needed to be responsible to the probable users in different disciplines such as; Reconnaissance, City Planning, Topographic mapping, Transportation, Environment control, disaster management and etc… In Chapter 6, all the data quality assessment and protocols will be implemented into the pre-filtered, proposed datasets. In the final scores and ranking result, each datasets will have a value corresponding to their quality according to the sets of rules that are defined in previous chapter. In last steps, there will be a vector of weight that is derived from the questions that has to be answered by user with reference to the project in hand in order to finalize the most appropriate selection of Free and Open Source Data. This Data quality preference has to be defined by identifying a set of weight vector, and then they have to be applied to the quality matrix in order to get a final quality scores and ranking. At the end of this chapter there will be a section presenting data sets utilization in various projects such as “ Early Impact Analysis” as well as “Extreme Rainfall Detection System (ERDS)- version 2” performed by ITHACA. Finally, in conclusion, the important criteria, as well as future trend in GIS software are discussed and at the end recommendations will be presented