Open Geospatial Standards Research Articles

Transport sustainability indicators for an enhanced urban analytics data infrastructure

This paper examines capabilities of a new Spatial Data Infrastructure called Urban Analytics Data Infrastructure (UADI11Spatial Data Infrastructure called Urban Analytics Data Infrastructure), through deriving and evaluating transport sustainability indicators. The UADI was developed in Australia to support multi-disciplinary and cross-jurisdictional analytics to overcome the challenges related to model generalisation, data accessibility, data integration, data heterogeneity and city performance benchmarking. In this paper, the UADI were evaluated through 5 technical lenses of: data accessibility; integration; harmonisation; data reliability and model reliability. The paper shows that using open geospatial standards in UADI enabled transport sustainability indicators to be derived through accessing and integrating different spatial data layers and the process of mapping data to ontology facilitated data harmonisation. In addition, the input data, tool processing, and output of ontology-based transport sustainability indicators could be traced in UADI, which addresses the challenge of model and data reliability. The paper highlights the role of spatial data infrastructures in decision support systems for uncertainty analysis and promoting smart cities and resilient environment.

Overview of the OGC CDB Standard for 3D Synthetic Environment Modeling and Simulation

Recent advances in sensor and platform technologies, such as satellite systems, unmanned aerial vehicles (UAV), manned aerial platforms, and ground-based sensor networks have resulted in massive volumes of data being produced and collected about the earth. Processing, managing, and analyzing these data is one of the main challenges in 3D synthetic representation used in modeling and simulation (M&S) of the natural environment. M&S devices, such as flight simulators, traditionally require a variety of different databases to provide a synthetic representation of the world. M&S often requires integration of data from a variety of sources stored in different formats. Thus, for simulation of a complex synthetic environment, such as a 3D terrain model, tackling interoperability among its components (geospatial data, natural and man-made objects, dynamic and static models) is a critical challenge. Conventional approaches used local proprietary data models and formats. These approaches often lacked interoperability and created silos of content within the simulation community. Therefore, open geospatial standards are increasingly perceived as a means to promote interoperability and reusability for 3D M&S. In this paper, the Open Geospatial Consortium (OGC) CDB Standard is introduced. “CDB” originally referred to Common DataBase, which is currently considered as a name with no abbreviation in the OGC community. The OGC CDB is an international standard for structuring, modeling, and storing geospatial information required in high-performance modeling and simulation applications. CDB defines the core conceptual models, use cases, requirements, and specifications for employing geospatial data in 3D M&S. The main features of the OGC CDB Standard are described as the run-time performance, full plug-and-play interoperable geospatial data store, usefulness in 3D and dynamic simulation environment, ability to integrate proprietary and open-source data formats. Furthermore, compatibility with the OGC standards baseline reduces the complexity of discovering, transforming, and streaming geospatial data into the synthetic environment and makes them more widely acceptable to major geospatial data/software producers. This paper includes an overview of OGC CDB version 1.0, which defines a conceptual model and file structure for the storage, access, and modification of a multi-resolution 3D synthetic environment data store. Finally, this paper presents a perspective of future versions of the OGC CDB and what the steps are for humanizing the OGC CDB standard with the other OGC/ISO standards baseline.

New frontiers on open standards for geo-spatial science

The process of sharing of data has become easier than ever with the advancement of cloud computing and software tools. However, big challenges remain such as efficient handling of big geospatial data, supporting and sharing of crowd sourced/citizen science data, integration with semantic heterogeneity, and inclusion of agile processes for continuous improvement of geospatial technology. This paper discusses the new frontiers regarding these challenges and the related work performed by the Open Geospatial Consortium, the world leading organization focused on developing open geospatial standards that “geo-enable” the Web, wireless, and location-based services and mainstream IT.

BIG GEO DATA MANAGEMENT: AN EXPLORATION WITH SOCIAL MEDIA AND TELECOMMUNICATIONS OPEN DATA

Abstract. The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

BIG GEO DATA MANAGEMENT: AN EXPLORATION WITH SOCIAL MEDIA AND TELECOMMUNICATIONS OPEN DATA

The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is necessary to integrate high-performance solutions, preferably internet based, to achieve the goals. Secondly, the problems of heterogeneity and inconsistency in geospatial data are well known and affect the data integration process, but is particularly problematic for Big Geo Data. Therefore, Big Geo Data integration will be one of the most challenging issues to solve. With these applications, we demonstrate that is possible to provide processed Big Geo Data to common users, using open geospatial standards and technologies. NoSQL databases like MongoDB and frameworks like RASDAMAN could offer different functionalities that facilitate working with larger volumes and more heterogeneous geospatial data sources.

A Geospatial Web Service Approach for Creating On-Demand Cropland Data Layer Thematic Maps

<abstract><title><italic>Abstract.</italic></title> A thematic map displays the spatial patterns of one or more data themes for a specified geographic area. Thematic maps are used, for example, in geospatial data distribution and exchange, multi-source geospatial data aggregation, visual communication, and decision support. The latest web technologies and open geospatial standards make it feasible to produce on-demand thematic maps in an internet environment. This article describes a geospatial web service approach for creating customized thematic maps of cropland data layer (CDL) data. The design and implementation of the geospatial web service using open-source software are described in detail. The service meets the requirements of producing, sharing, and printing CDL thematic maps for the areas of interest without requiring additional mapping software. Examples of service invocation are given to demonstrate the applicability of the proposed approach. This service offers a new dissemination channel to distribute CDL data to the general public. Feedback from users in the agricultural community shows that the implemented web service has greatly improved the availability, customization, and timeliness of CDL thematic maps, reduced operational resource requirements and cost, facilitated geospatial data distribution and usability, and broadened the reach of geospatial cropland data products. In the first 20 months of availability, users have produced over 10,000 thematic maps.

Geospatial services in the Cloud

Data semantics play an extremely significant role in spatial data infrastructures by providing semantic specifications to geospatial data and enabling in this way data sharing and interoperability. By applying, on the fly, composite geospatial processes on the above data it is possible to produce valuable geoinformation over the web directly available and applicable to a wide range of geo-activities of significant importance for the research and industry community. Cloud computing may enable geospatial processing since it refers to, among other things, efficient computing resources providing on demand processing services. In this context, we attempt to provide a design and architectural framework for web applications based on open geospatial standards. Our approach includes, in addition to geospatial processing, data acquisition services that are essential especially when dealing with satellite images and applications in the area of remote sensing and similar fields. As a result, by putting in a common framework all data and geoprocesses available in the Cloud, it is possible to combine the appropriate services in order to produce a solution for a specific need.

Prototyping an online wetland ecosystem services model using open model sharing standards

Great interest currently exists for developing ecosystem models to forecast how ecosystem services may change under alternative land use and climate futures. Ecosystem services are diverse and include supporting services or functions (e.g., primary production, nutrient cycling), provisioning services (e.g., wildlife, groundwater), regulating services (e.g., water purification, floodwater retention), and even cultural services (e.g., ecotourism, cultural heritage). Hence, the knowledge base necessary to quantify ecosystem services is broad and derived from many diverse scientific disciplines. Building the required interdisciplinary models is especially challenging as modelers from different locations and times may develop the disciplinary models needed for ecosystem simulations, and these models must be identified and made accessible to the interdisciplinary simulation. Additional difficulties include inconsistent data structures, formats, and metadata required by geospatial models as well as limitations on computing, storage, and connectivity. Traditional standalone and closed network systems cannot fully support sharing and integrating interdisciplinary geospatial models from variant sources. To address this need, we developed an approach to openly share and access geospatial computational models using distributed Geographic Information System (GIS) techniques and open geospatial standards. We included a means to share computational models compliant with Open Geospatial Consortium (OGC) Web Processing Services (WPS) standard to ensure modelers have an efficient and simplified means to publish new models. To demonstrate our approach, we developed five disciplinary models that can be integrated and shared to simulate a few of the ecosystem services (e.g., water storage, waterfowl breeding) that are provided by wetlands in the Prairie Pothole Region (PPR) of North America.

Open geospatial technology standards and their potential in plant pest risk management—GPS-enabled mobile phones utilising open geospatial technology standards Web Feature Service Transactions support the fighting of fire blight in Norway

The threat posed by invasive alien species to global crop production and biodiversity is recognized as one of the major drivers of global change today. Here we report on a successful implementation and operational use of new technology to support risk- and crisis management in the case of fighting an invasive plant disease. The open geospatial standards Web Feature Service Transaction (WFS-T) was implemented in software for GPS-enabled mobile phones communicating with a GeoServer backend in order to track down and eradicate disease outbreaks and susceptible host plants. This new technology offering georeferenced events online from field provides new possibilities for real time action in risk and crisis management.

Development of track log and point of interest management system using Free and Open Source Software

Recent advanced performance of low-cost Global Positioning System (GPS) and GPS-enabled cell phones has contributed a great deal to the development of location-aware services and systems. High-speed broadband technology has promoted collaborative projects such as OpenStreetMap or other User Generated Contents services. In this research, a Web-based prototype system for GPS track log and point of interest (POI) management was developed to archive a collaborative framework in field surveys. The main function of the system can be separated into three parts: data collection, data management, and data quality enhancement. The system supports real-time data collection for the future ubiquitous environment and also can monitor real-time GPS positions. This research shows functionalities that can minimize GPS errors using Dilution of Precision filtering and data quality enhancing techniques using the Douglas-Peucker algorithm and PgRouting. The research introduces a system that provides an interoperable framework in which to work with other geospatial services through open geospatial standards.