Web Processing Service Research Articles

PROJECTS BASED ON THE WEB PROCESSING SERVICE FRAMEWORK BIRDHOUSE

Abstract. Birdhouse is a collaborative project open for the community to participate. It is a software framework containing a collection of Web Processing Services (WPS). The deployed algorithms are focusing on Earth Systems and environmental data processing with the philosophy of streamlining the software development and deployment. By supporting climate, earth observation and biodiversity data and processes, Birdhouse can be used in a wide array of Earth sciences projects and workflows. The core benefit of this project is to allow the seamless use of climate services developed by a diverse network of national meteorological offices, regional climate service providers, academics, not-for-profit research centers and private industry. As governments move toward open-data policies, there will be a need for analytical services that extract value out of the deluge of information. Using an interoperable software architecture, institutions can provide both data and services allowing users to process the data remotely from a laptop, instead of having to acquire and maintain large storage infrastructures.

Geospatial Web Services in Real Estate Information System

Since the data of cadastral records are of great importance for the economic development of the country, they must be well structured and organized. Records of real estate on the territory of Serbia met many problems in previous years. To prevent problems and to achieve efficient access, sharing and exchange of cadastral data on the principles of interoperability, domain model for real estate is created according to current standards in the field of spatial data. The resulting profile of the domain model for the Serbian real estate cadastre is based on the current legislation and on Land Administration Domain Model (LADM) which is specified in the ISO19152 standard. Above such organized data, and for their effective exchange, it is necessary to develop a model of services that must be provided by the institutions interested in the exchange of cadastral data. This is achieved by introducing a service-oriented architecture in the information system of real estate cadastre and with that ensures efficiency of the system. It is necessary to develop user services for download, review and use of the real estate data through the web. These services should be provided to all users who need access to cadastral data (natural and legal persons as well as state institutions) through e-government. It is also necessary to provide search, view and download of cadastral spatial data by specifying geospatial services. Considering that real estate contains geometric data for parcels and buildings it is necessary to establish set of geospatial services that would provide information and maps for the analysis of spatial data, and for forming a raster data. Besides the theme Cadastral parcels, INSPIRE directive specifies several themes that involve data on buildings and land use, for which data can be provided from real estate cadastre. In this paper, model of geospatial services in Serbia is defined. A case study of using these services to estimate which household is at risk of flooding using the Web Processing Service (WPS) spatial analysis is described.

Online Spatial Data Analysis and Algorithm Development for Geo-scientific Applications Using Remote Sensing Data

Availability of geospatial data from online data repositories and geoportals are increasing with rapid pace. For better utilization of these heterogeneous data sets, it is required to give more attention to develop web based algorithms as well as geocomputation models that allows high-level information extraction from distributed nature of geodata. The Open Geospatial Consortium specification such as Web Processing Service (WPS) provides the solutions for integrating remote sensing data for the computation of algorithms from distributed sources in multi-user environment. The main purpose of present study is to develop and implement a WPS based geoprocesses and analysis approach using Online Image Processing System. The software framework developed during this study is a platform independent solution which is developed using free and open source technology. As a demonstrative case study, the Normalized Difference Vegetation Index and Normalized Difference Built-up Index algorithms are annotated for automatic deployment in geoscientific processing workflow environment. The Sentinel-2 data sets and polygon geometry as region of interest are stored and published as web services for input dataset in application server repository. A case study for Champawat district of Uttarakhand state of India has been demonstrated to identify vegetation greenness and built-up sites during the summer season. In order to access the functionalities of the online application, an interactive and responsive Graphical User Interface is developed in web GIS environment. The proposed approach can be very effective and useful to address the key challenges in commercial desktop based software for geoprocessing and analysis of remote sensing data.

Web processing service for climate impact and extreme weather event analyses. Flyingpigeon (Version 1.0)

Analyses of extreme weather events and their impacts often requires big data processing of ensembles of climate model simulations. Researchers generally proceed by downloading the data from the providers and processing the data files “at home” with their own analysis processes. However, the growing amount of available climate model and observation data makes this procedure quite awkward. In addition, data processing knowledge is kept local, instead of being consolidated into a common resource of reusable code. These drawbacks can be mitigated by using a web processing service (WPS). A WPS hosts services such as data analysis processes that are accessible over the web, and can be installed close to the data archives.We developed a WPS named ‘flyingpigeon’ that communicates over an HTTP network protocol based on standards defined by the Open Geospatial Consortium (OGC), to be used by climatologists and impact modelers as a tool for analyzing large datasets remotely.Here, we present the current processes we developed in flyingpigeon relating to commonly-used processes (preprocessing steps, spatial subsets at continent, country or region level, and climate indices) as well as methods for specific climate data analysis (weather regimes, analogues of circulation, segetal flora distribution, and species distribution models). We also developed a novel, browser-based interactive data visualization for circulation analogues, illustrating the flexibility of WPS in designing custom outputs.Bringing the software to the data instead of transferring the data to the code is becoming increasingly necessary, especially with the upcoming massive climate datasets.

An automated and optimized approach for online spatial biodiversity model: a case study of OGC web processing service

An online spatial biodiversity model (SBM) for optimized and automated spatial modelling and analysis of geospatial data is proposed, which is based on web processing service (WPS) and web service orchestration (WSO) in parallel computing environment. The developed model integrates distributed geospatial data in geoscientific processing workflow to compute the algorithms of spatial landscape indices over the web using free and open source software. A case study for Uttarakhand state of India demonstrates the model outputs such as spatial biodiversity disturbance index (SBDI) and spatial biological richness index (SBRI). In order to optimize and automate, an interactive web interface is developed using participatory GIS approaches for implementing fuzzy AHP. In addition, sensitivity analysis and geosimulation experiments are also performed under distributed GIS environment. Results suggest that parallel algorithms in SBM execute faster than sequential algorithms and validation of SBRI with biological diversity shows significant correlation by indicating high R2 values.

DEVELOPING A SPATIAL PROCESSING SERVICE FOR AUTOMATIC CALCULATION OF STORM INUNDATION

Abstract. With the increase in urbanization, the surface of earth and its climate are changing. These changes resulted in more frequent floodingand storm inundation in urban areas. The challenges of flooding can be addressed through several computational procedures. Due to its numerous advantages, accessible web services can be chosen as a proper format for determining the storm inundation. Web services have facilitated the integration and interactivity of the web applications. Such services made the interaction between machines more feasible. Web services enable the heterogeneous software systems to communicate with each other. A Web Processing Service (WPS) makes it possible to process spatial data with different formats. In this study, we developed a WPS to automatically calculate the amount of storm inundation caused by rainfall in urban areas. The method we used for calculating the storm inundation is based on a simplified hydrologic model which estimates the final status of inundation. The simulation process and water transfer between subcatchments are carried out respectively, without user’s interference. The implementation of processing functions in a form of processing web services gives the capability to reuse the services and apply them in other services. As a result, it would avoid creating the duplicate resources.

SIG-CLIMALG: The geographical dimension of climate data in Algeria

In Algeria, implementation, monitoring and exploitation of climate data, development and dissemination of periodic publications and climate assistance to users, are tasks of the National Climatological Centre (NCC). Moreover, the effective management of this large volume of data requires the use of new technologies such as Geographic Information Systems (GIS). In this context, the aim of the present work is to develop an IT solution on the Web, based on the use of Geographic Information Systems. The approach consists, first, to design databases to store various data used (climatic and topographic) considering the functional needs such as the dissemination of data in several modes (read, download) and the possibility direct data access from remote locations. Then the choice of technology and tools to implement a mapping solution that will allow, in addition to the functionality of storage and distribution of different types of data, analysis of climate observations by using a web service whitin the WPS (Web Processing Service) standard.

Cloud computing in a distributed e‐infrastructure using the web processing service standard

SummaryNew Science paradigms have recently evolved to promote open publication of scientific findings as well as multi‐disciplinary collaborative approaches to scientific experimentation. These approaches can face modern scientific challenges but must deal with large quantities of data produced by industrial and scientific experiments. These data, so‐called Big Data, require to introduce new computer science systems to help scientists cooperate, extract information, and possibly produce new knowledge out of the data. E‐infrastructures are distributed computer systems that foster collaboration between users and can embed distributed and parallel processing systems to manage big data. However, in order to meet modern Science requirements, e‐Infrastructures impose several requirements to computational systems in turn, eg, being economically sustainable, managing community‐provided processes, using standard representations for processes and data, managing big data size and heterogeneous representations, supporting reproducible Science, collaborative experimentation, and cooperative online environments, managing security and privacy for data and services. In this paper, we present a cloud computing system (gCube DataMiner) that meets these requirements and operates in an e‐Infrastructure, while sharing characteristics with state‐of‐the‐art cloud computing systems. To this aim, DataMiner uses the web processing service standard of the open geospatial consortium and introduces features like collaborative experimental spaces, automatic installation of processes and services on top of a flexible and sustainable cloud computing architecture. We compare DataMiner with another mature cloud computing system and highlight the benefits our system brings, the new paradigms requirements it satisfies, and the applications that can be developed based on this system.

Semantic and syntactic interoperability in online processing of big Earth observation data

ABSTRACTThe challenge of enabling syntactic and semantic interoperability for comprehensive and reproducible online processing of big Earth observation (EO) data is still unsolved. Supporting both types of interoperability is one of the requirements to efficiently extract valuable information from the large amount of available multi-temporal gridded data sets. The proposed system wraps world models, (semantic interoperability) into OGC Web Processing Services (syntactic interoperability) for semantic online analyses. World models describe spatio-temporal entities and their relationships in a formal way. The proposed system serves as enabler for (1) technical interoperability using a standardised interface to be used by all types of clients and (2) allowing experts from different domains to develop complex analyses together as collaborative effort. Users are connecting the world models online to the data, which are maintained in a centralised storage as 3D spatio-temporal data cubes. It allows also non-experts to extract valuable information from EO data because data management, low-level interactions or specific software issues can be ignored. We discuss the concept of the proposed system, provide a technical implementation example and describe three use cases for extracting changes from EO images and demonstrate the usability also for non-EO, gridded, multi-temporal data sets (CORINE land cover).

Coupling sensor observation services and web processing services for online geoprocessing in water dam monitoring

ABSTRACTNovel sensor technologies are rapidly emerging. They enable a monitoring and modelling of our environment in a level of detail that was not possible a few years ago. However, while the raw data produced by these sensors are useful to get a first overview, it usually needs to be post-processed and integrated with other data or models in different applications. In this paper, we present an approach for integrating several geoprocessing components in the TaMIS water dam monitoring system developed with the Wupperverband, a regional waterbody authority in Germany. The approach relies upon the OGC Web Processing Service and is tightly coupled with Sensor Observation Service instances running at the Wupperverband. Besides implementing the standardized XML-based interface, lightweight REST APIs have been developed to ease the integration with thin Web clients and other Web-based components. Using this standards-based approach, new processing facilities can be easily integrated and coupled with different observation data sources.

Enhancing the OGC WPS interface with GeoPipes support for real-time geoprocessing

ABSTRACTReal-time geospatial information is used in various applications such as risk management or alerting services. Especially, the rise of new sensing technologies also increases the demand for processing the data in real time. Today’s spatial data infrastructures, however, do not meet the requirements for real-time geoprocessing. The OpenGIS® Web Processing Service (WPS) is not designed to process real-time workflows. It has some major drawbacks in asynchronous processing and cannot handle (geo) data streams out of the box. In previous papers, we introduced the GeoPipes approach to share spatiotemporal data in real time. We implemented the concept extending the Message Queue and Telemetry Transport (MQTT) protocol by a spatial and temporal dimension, which we call GeoMQTT. In this paper, we demonstrate the integration of the GeoPipes idea in the WPS interface to expose standardized real-time geoprocessing services. The proof of the concept is illustrated in some exemplary real-time geo processes.

Approach to Facilitating Geospatial Data and Metadata Publication Using a Standard Geoservice

Nowadays, the existence of metadata is one of the most important aspects of effective discovery of geospatial data published in Spatial Data Infrastructures (SDIs). However, due to lack of efficient mechanisms integrated in the data workflow, to assist users in metadata generation, a lot of low quality and outdated metadata are stored in the catalogues. This paper presents a mechanism for generating and publishing metadata through a publication service. This mechanism is provided as a web service implemented with a standard interface called a web processing service, which improves interoperability between other SDI components. This work extends previous research, in which a publication service has been designed in the framework of the European Directive Infrastructure for Spatial Information in Europe (INSPIRE) as a solution to assist users in automatically publishing geospatial data and metadata in order to improve, among other aspects, SDI maintenance and usability. Also, this work adds more extra features in order to support more geospatial formats, such as sensor data.

W3C PROV to describe provenance at the dataset, feature and attribute levels in a distributed environment

Provenance, a metadata component referring to the origin and the processes undertaken to obtain a specific geographic digital feature or product, is crucial to evaluate the quality of spatial information and help in reproducing and replicating geospatial processes. However, the heterogeneity and complexity of the geospatial processes, which can potentially modify part or the complete content of datasets, make evident the necessity for describing geospatial provenance at dataset, feature and attribute levels. This paper presents the application of W3C PROV, which is a generic specification to express provenance records, for representing geospatial data provenance at these different levels. In particular, W3C PROV is applied to feature models, where geospatial phenomena are represented as individual features described with spatial (point, lines, polygons, etc.) and non-spatial (names, measures, etc.) attributes.This paper first analyses the potential for representing geospatial provenance in a distributed environment at the three levels of granularity using ISO 19115 and W3C PROV models. Next, an approach for applying the generic W3C PROV provenance model to the geospatial environment is presented. As a proof of concept, we provide an application of W3C PROV to describe geospatial provenance at the feature and attribute levels. The use case presented consists of a conflation of the U.S. Geological Survey dataset with the National Geospatial-Intelligence Agency dataset. Finally, an example of how to capture the provenance resulting from workflows and chain executions with PROV is also presented. The application uses a web processing service, which enables geospatial processing in a distributed system and allows to capture the provenance information based on the W3C PROV ontology at the feature and attribute levels.

Web processing service integrated with mobile application to identify suitable grain storage facility location

Heavy rains causing a flood like situation affect the grains stored in godowns. To save the grains from floods, they have to be transferred to safer places. Historical flood layers derived using the remote sensing data can be helpful in determining the safe place for transferring the grains from the flood affected godowns. Analyzing of satellite data for determining the safer locations needs specialized skills and GIS software’s. To overcome this, a web based information system, capable of organizing, hosting, and sharing of the flood layers and other spatial datasets as open geospatial consortium compatible web processing services is needed. For transferring the grains, the management must have the existing stock information of the flood affected godown as well as stock information of the godown where the grains are planned to be shifted. Mobile application, capable of reporting the stock information along with the godown location and ground photographs can be an efficient solution to do so. This paper presents work on web processing services integrated with mobile application using open source technologies to identify suitable grain godowns location.

Linkage of OGC WPS 2.0 to the e-Government Standard Framework in Korea: An Implementation Case for Geo-Spatial Image Processing

There are many cases wherein services offered in geospatial sectors are integrated with other fields. In addition, services utilizing satellite data play important roles in daily life and in sectors such as environment and science. Therefore, a management structure appropriate to the scale of the system should be clearly defined. The motivation of this study is to resolve issues, apply standards related to a target system, and provide practical strategies with a technical basis. South Korea uses the e-Government Standard Framework, using the Java-based Spring framework, to provide guidelines and environments with common configurations and functions for developing web-based information systems for public services. This web framework offers common sources and resources for data processing and interface connection to help developers focus on business logic in designing a web system. In this study, a geospatial image processing system—linked with the Open Geospatial Consortium (OGC) Web Processing Service (WPS) 2.0 standard for real geospatial information processing, and based on this standard framework—was designed and built utilizing fully open sources. This is the first case of implementation based on WPS 2.0 running on the e-Government Standard Framework. Establishing a standard for its use will be important, and the system built in this study can serve as a reference for the foundational architecture in building geospatial web service systems with geodata-processing functionalities in government agencies.

An integrated architectural framework for geoprocessing in cloud environment

Spatial data infrastructure had ensured the availability of geospatial data over the local and global boundaries to be used in more flexible and efficient manner. With the enhancement in web service technologies the need of geoprocessing the geospatial data from various heterogeneous sources over the web, to produce valuable information is of great interest among research community. Towards this implementation of geoprocessing web, open geospatial consortium (OGC) has provided several web processing service standards for handling the geoprocess over the web. Most of the current existing geoprocessing web platforms inherit the capabilities of these web processing service standards and protocols define by OGC. However there exist several challenges that includes, growing intensity of data, high performance computing, real time data processing, and to provide robust, reliable, and cost effective geoprocess services to the end user. The future of geo-processing lies in the adoption of cloud computing to overcome the existing challenges. Characteristics of cloud computing including scalability, elasticity, pay-per-use, and self provisioning, offers a more reliable, on demand, and cost effective geoprocessing services to its end user. In this context the study makes an attempt to analyze the current state of geoprocessing services over cloud, with exiting GIS Cloud platform solutions in the literature to counter the challenges of GIS cloud. Furthermore, study proposes a design and architectural framework for geoprocessing over Cloud platform, based on OGC standards. The architecture includes developing geoprocessing service, and data management services as an essential part while storing/processing the geospatial data from heterogeneous sources. The architecture model including data management and geoprocessing can be bind together in a cloud platform to have an on demand service as per the requirement of end user.

ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture

The inversion of remote sensing images is crucial for soil moisture mapping in precision agriculture. However, the large size of remote sensing images complicates their management. Therefore, this study proposes a remote sensing observation sharing method based on cloud computing (ROSCC) to enhance remote sensing observation storage, processing, and service capability. The ROSCC framework consists of a cloud computing-enabled sensor observation service, web processing service tier, and a distributed database tier. Using MongoDB as the distributed database and Apache Hadoop as the cloud computing service, this study achieves a high-throughput method for remote sensing observation storage and distribution. The map, reduced algorithms and the table structure design in distributed databases are then explained. Along the Yangtze River, the longest river in China, Hubei Province was selected as the study area to test the proposed framework. Using GF-1 as a data source, an experiment was performed to enhance earth observation data (EOD) storage and achieve large-scale soil moisture mapping. The proposed ROSCC can be applied to enhance EOD sharing in cloud computing context, so as to achieve soil moisture mapping via the modified perpendicular drought index in an efficient way to better serve precision agriculture.

Agent-as-a-service-based geospatial service aggregation in the cloud: A case study of flood response

An Agent-as-a-Service (AaaS)-based geospatial service aggregation is proposed to build a more efficient, robust and intelligent geospatial service system in the Cloud for flood emergency response. It involves an AaaS infrastructure, encompassing the mechanisms and algorithms for geospatial Web Processing Service (WPS) generation, geoprocessing and aggregation. The method has the following advantages: 1) it allows separately hosted services and data to work together, avoiding transfers of large volumes of spatial data over the network; 2) it enriches geospatial service resources in the distributed environment by utilizing the agent cloning, migration and service regeneration capabilities of the AaaS, solving issues associated with lack of geospatial services to a certain extent; 3) it enables the migration of services to target nodes to finish a task, strengthening decentralization and enhancing the robustness of geospatial service aggregation; and 4) it helps domain experts and authorities solve interdisciplinary emergency issues using various Agent-generated geospatial services.

Spatial analysis in WebGIS

In most of WebGIS applications, user interactions mainly concentrate on map data editing or data querying. Another aspect with paying less attention is to allow user doing spatial analysis. Spatial analysis can be implemented on server side, by using WPS (Web Processing Service), or on client side, by using JSTS (JavaScript Topology Suite). As an contribution to this aspect, this paper shows how to use to use WPS service of Geoserver and JSTS JavaScript library to carry on spatial analysis activities. A WebGIS system - based on the open source Geoserver and OpenLayers - has been developed and used to perform spatial analysis, searching for spatial features that have spatial relationships with other spatial features. Experiment result shows that the use of WPS and JSTS to perform spatial processing over the internet is practically doable.

New implementation of OGC Web Processing Service in Python programming language. PyWPS-4 and issues we are facing with processing of large raster data using OGC WPS

Abstract. The OGC® Web Processing Service (WPS) Interface Standard provides rules for standardizing inputs and outputs (requests and responses) for geospatial processing services, such as polygon overlay. The standard also defines how a client can request the execution of a process, and how the output from the process is handled. It defines an interface that facilitates publishing of geospatial processes and client discovery of processes and and binding to those processes into workflows. Data required by a WPS can be delivered across a network or they can be available at a server. PyWPS was one of the first implementations of OGC WPS on the server side. It is written in the Python programming language and it tries to connect to all existing tools for geospatial data analysis, available on the Python platform. During the last two years, the PyWPS development team has written a new version (called PyWPS-4) completely from scratch. The analysis of large raster datasets poses several technical issues in implementing the WPS standard. The data format has to be defined and validated on the server side and binary data have to be encoded using some numeric representation. Pulling raster data from remote servers introduces security risks, in addition, running several processes in parallel has to be possible, so that system resources are used efficiently while preserving security. Here we discuss these topics and illustrate some of the solutions adopted within the PyWPS implementation.