PostGIS-Based Heterogeneous Sensor Database Framework for the Sensor Observation Service

Environmental monitoring and management systems in most cases deal with models and spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic services such as the Geo-Web Services. Thus, data have to be pulled from different databases and transferred over the network before they are fused and processed on the service middleware. This process is very massive and unnecessary communication and work load on the service. Massive work load in large raster downloads from flat-file raster data sources each time a request is made and huge integration and geo-processing work load on the service middleware which could actually be better leveraged at the database level. In this paper, we propose and present a heterogeneous sensor database framework or model for integration, geo-processing and spatial analysis of remote and in-situ sensor observations at the database level.  And how this can be integrated in the Sensor Observation Service, SOS to reduce communication and massive workload on the Geospatial Web Services and as well make query request from the user end a lot more flexible

Geo-Based Image Analysis Service In Open Source Cloud Computing Environment

Globally, cloud computing is one of huge trends in ICT communities, exerting its influence on the most information application fields including geo-spatial domain. In general, cloud computing services are being regarded as commercial or public internet data center or infrastructure of computing resources. However, there is no limitation for applications of cloud computing. Especially, it is on the early developing stage in geo-based applications. This project is to present a practical application for geo-based image processing and analysis on open source cloud environment. OpenStack Juno version was applied for open source cloud computing environment. On this cloud environment, PostgreSQL and Django were used as open source for metadata server and web framework, respectively. For geo-based image processing server, OTB was used with GDAL for image manipulating. Some image processing algorithms were implemented in this cloud environment, and provides as a web service for public users. Web-accessible users in this cloud service do not need any software installation and downloading data sets. This full open source approach is expected to be an element geo-spatial information processing linked with cloud computing service

Geoprocessing Desktop Application for INCOIS Web Services

Geoprocessing is based on a framework of data transformation. The fundamental purpose of geoprocessing is to allow us to automate the GIS tasks. Almost all uses of GIS involve the repetition of work, and this creates the need for methods to automate, document, and share multiple-step procedures known as workflows. Geoprocessing supports the automation of workflows by providing a rich set of tools and a mechanism to combine a series of tools in a sequence of operations using models and scripts. INCOIS has started and automated the Geoprocessing framework for the INCOIS Web Services. All these tasks and resulted new datasets is a need and to be represented on a user-friendly interface in order to analyze and do further rectification and changes.A user-friendly interface using geo-spatial technologies and java based desktop application is a handy tool for processing and analyzing the spatial data of INCOIS Web Services viz., Argo Float Data and value-added Products, Potential Fishing Zone (PFZ), Ocean State Forecast (OSF), Tsunami Advisories to download and generate spatial data, layers, images with the application for identification, analyzing, editing, geoprocessing of Argo floats positions, trajectories, fishing zones, wave directions, wind directions, etc. This application provides with all basic GIS functionalities like Zoom In and Zoom out, Pan, measurement, pixel value information, etc. The application also helps in exporting the spatial data into user required format as well as creation of spatial maps with identified fishing zones, Argo float positions, etc

A geographical information system for wild fire management

he CROSS-Fire project focus on developing a grid-based framework for wild fire management using FireStation (FS) as a standalone application that simulates the fire spread over complex topography. The overall software development is made of several components: client applications, which request geo-referenced data and fire spread simulation, Spatial Data Infrastructures (SDI), which provide geo-referenced data, and the GRID, which gives support to the computational and data storage requirements. Herein we present the central WPS (Web Processing System) layer developed to support the interaction between all components of the architecture. This OGC-WS compatible layer provides the mechanism to access the grid facilities for processing and data management and including all the algorithms, calculation, or model that operates on spatially referenced data, also mediating communication with the FS console. We also describe the work that has been done to provide FS with dynamic fuel maps, by using an OGC-WCS suite of services and satellite data. This task complements the previous integration of dynamic data from meteorological stations using OGC-SWE services

An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities

Rapid urbanization in developing countries has been observed to be relatively high in the last two decades, especially in the Asian and African regions. Although many researchers have made efforts to improve the understanding of the urbanization trends of various cities in Asia and Africa, the absence of platforms where local stakeholders can visualize and obtain processed urbanization data for their specific needs or analysis, still remains a gap. In this paper, we present an Internet-based GIS platform called MEGA-WEB. The Platform was developed in view of the urban planning and management challenges in developing countries of Asia and Africa due to the limited availability of data resources, effective tools, and proficiency in data analysis. MEGA-WEB provides online access, visualization, spatial analysis, and data sharing services following a mashup framework of the MEGA-WEB Geo Web Services (GWS), with the third-party map services using HTML5/JavaScript techniques. Through the integration of GIS, remote sensing, geo-modelling, and Internet GIS, several indicators for analyzing urbanization are provided in MEGA-WEB to give diverse perspectives on the urbanization of not only the physical land surface condition, but also the relationships of population, energy use, and the environment. The design, architecture, system functions, and uses of MEGA-WEB are discussed in the paper. The MEGA-WEB project is aimed at contributing to sustainable urban development in developing countries of Asia and Africa

The template for a Semantic SensorThings API with the GloSIS use case

Since 2016 the Open Geospatial Consortium (OGC) has embarked on a new specification paradigm for web access to geo-spatial data. Moving on from the SOAP/XML pattern underlying the many web services it specified earlier, the OGC has since issued novel standards based on ReST API specifications such as OpenAPI and OData. In tandem, data encoding formats considered in the OGC’s standards have been greatly expanded. This new direction has greatly aligned the OGC’s work with the Semantic Web, the collection of specifications for data on the web based on the Resource Description Framework (RDF) issued by the World Wide Web Consortium (W3C).In this context, this communication explores the SensorThings API specification as means to serve environmental data in standardised fashion. With concrete examples, it demonstrates how the adoption of the Semantic Web in Spatial Data Infrastructures opens up a fresh approach to the development and deployment of data services, especially those in which domain data is deeply structured. Two use cases are reviewed herewith: the GloSIS web ontology for soil data exchange and the Iliad Ocean Digital Twin

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web

Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions