Open Geospatial Consortium Standards Research Articles

Composition of factors for local heat adaptation measures at the local level in cities of the mid-latitude – An approach for the south-west of Germany

Traditional heat health warning systems focus on severe and extreme heat events at the district or regional level, often overlooking localized risk and protective factors such as healthcare access and urban green spaces. This approach considers less the varying impacts of heat within cities, including the phenomenon of Urban Heat Islands (UHIs) and the diverse needs of different populations. To address these shortcomings, a need for the development of an Urban Heat Health Warning and Information System (UHHWIS) that operates within the framework of Heat Health Action Plans is needed. Such a system integrates national acute heat health warnings with city-specific assessments of UHI effects and other relevant factors. The technical implementation of the UHHWIS involves the calculation and preprocessing of basic factors such as the Normalised Difference Vegetation Index (NDVI), imperviousness, and UHI intensity. Additionally, further factors are assessed, spatially processed, and provided in accordance with Open Geospatial Consortium (OGC) standards. An iso-area analysis is conducted to evaluate the accessibility of protective factors, such as urban green spaces, drinking wells, hospitals, physicians, and pharmacies, based on the city’s road topology. One crucial factor considered in the system is the casting of shadows, which is influenced by both time and location and facilitated through deck.gl. The developed template encompasses all these components into a unified system aimed at protecting vulnerable and risk groups, such as the elderly, through resilient, climate-adapted urban planning. The system provides warnings and information tailored to the urban morphology and prevailing conditions, complemented by a catalogue of potential short- to long-term measures focused on behavioral changes and climate-resilient urban planning strategies. The template can be adapted for use in various European cities, offering valuable insights to decision-makers in city administration for mitigating thermal stress and enhancing resilience against urban heat nowadays and in future.

A multiple models-multiple users group GIS-based decision support system for land use problems

Group GIS-based Decision Support Systems (GGDSSs) have been used as effective tools to involve a particular group of individuals in the process of assessing alternative lands/locations for a particular use. Multiple Models-Multiple Users GGDSSs refer to the systems in which each decision maker may use a different decision model in the group assessment process. The variety and dynamic nature of individual decision models defined by decision makers raises the need for access to diverse sets of spatial data as well as GIS (Geographic Information System) and MCDA (Multicriteria Decision Analysis) functionalities. To overcome this problem, the study presents a Web service-based framework for Multiple Models-Multiple Users GGDSS using OGC (Open Geospatial Consortium) and XMCDA (XML-based Multicriteria Decision Analysis) standards. The usability assessment of the proposed GGDSS shows that majority of the users would like to use this system regularly (40 % strongly agree and 30 % agree), believe it is easy to use (35 % strongly agree and 35 % agree), state that the various functions in this system are well integrated (60 % strongly agree and 20 % agree), believe most people would learn to use it very quickly (60 % strongly agree and 20 % agree), and have a high level of confidence using the system (65 % strongly agree and 5 % agree).

EMODnet Biology: Unlocking European marine biodiversity data

EMODnet Biology (hosted and coordinated by the Flanders Marine Institute (VLIZ)) is one of the seven themes within the European Marine Observation and Data network (EMODnet). The EMODnet Biology consortium aims to facilitate the accessibility and usage of marine biodiversity data. With the principle of "collect once, use many times" at its core, EMODnet Biology fosters collaboration across various sectors, including research, policy-making, industry, and individual citizens, to enhance knowledge sharing and inform decision-making. EMODnet Biology focuses on providing free and open access to comprehensive historical and recent data on the occurrence of marine species and their traits in all European regional seas. It achieves this through partnerships and collaboration with diverse international initiatives, such as the World Register of Marine Species (WoRMS), Marine Regions and the European node of the Ocean Biodiversity Information System (EurOBIS) among others. By promoting the usage of the Darwin Core Standard (Wieczorek et al. 2012), EMODnet Biology fosters data interoperability and ensures seamless integration with wider networks such as the Global Biodiversity Information Facility (GBIF) and the Ocean Biodiversity Information System (OBIS), serving as a significant data provider of the latter, as it is responsible for most of its data generated in Europe. Since its inception, EMODnet Biology has undertaken actions covering various areas, including providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. providing access to marine biological data with spatio-temporal, taxonomic, environmental- and sampling-related information among others; developing an exhaustive data quality control tool based on the Darwin Core standard, the British Oceanographic Data Centre and Natural Environment Research Council Vocabulary Server (BODC NVS2) parameters and other controlled vocabularies used; creating and providing training courses to guide data providers; performing gap analyses to identify data quality and coverage shortcomings; creating and publishing marine biological distribution maps for various species or species groups; and interacting with international and European initiatives, projects and organizations. Furthermore, EMODnet Biology contributes to the overall EMODnet initiative, which covers multidisciplinary data and products. Thanks to the use of standard protocols and tools across disciplines, EMODnet Biology products can contribute to multidisciplinary analysis of pressures and impacts on key marine species and habitats, and, lastly, support a better management and planning of the maritime space. In conclusion, EMODnet Biology plays a pivotal role in biodiversity informatics by providing users with a wealth of accessible and reusable marine biodiversity data and products. Its collaborative approach, extensive partnerships, and adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles (Wilkinson et al. 2016) as well as to the Infrastructure for Spatial Information in Europe (INSPIRE) metadata technical guidelines (European Commission Joint Research Centre 2013) and the Open Geospatial Consortium (OGC) standards make it a valuable resource for advancing knowledge, informing policies, and supporting sustainable management of marine ecosystems.

LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation

ABSTRACT The rapid processing, analysis, and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have recently become a new trend. The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation, which ignores remote sensing data characteristics such as large image size, large-scale change, multiple data channels, and geographic knowledge embedding, thus impairing computational efficiency and accuracy. We construct a LuoJiaAI platform composed of a standard large-scale sample database (LuoJiaSET) and a dedicated deep learning framework (LuoJiaNET) to achieve state-of-the-art performance on five typical remote sensing interpretation tasks, including scene classification, object detection, land-use classification, change detection, and multi-view 3D reconstruction. The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application. In addition, LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium (OGC) standards for better developing and sharing Earth Artificial Intelligence (AI) algorithms and products on benchmark datasets. LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism, showing great potential in high-precision remote sensing mapping applications.

Toward Interoperable Multi-hazard Modeling: A Disaster Management System for Disaster Model Service Chain

A natural hazard-related disaster event often causes a series of secondary disasters, forming a disaster chain. Modeling the evolution of disaster chains in multi-hazard scenarios is crucial for risk governance and urban resilience. However, existing multi-hazard models are limited by complex model design and fixed disaster types, making it impossible to ensure flexible reactions to complex and diverse scenarios. This study presents a disaster management system for disaster model service chain (DMSC) to implement interoperable multi-hazard modeling. To achieve efficient model interaction in the DMSC, a management module is designed to normalize heterogeneous single-hazard models based on disaster system theory and the Open Geospatial Consortium standards, enabling them to be accessible, reusable, and interoperable. The normalized models are then adaptively orchestrated through an orchestration module to establish optimal executable DMSCs for different multi-hazard scenarios. Taking an earthquake disaster chain as a case study, we demonstrate that the disaster management system shows stable and flexible performance for multi-hazard modeling.

THE SPATIAL DATA INFRASTRUCTURE OF AN URBAN DIGITAL TWIN IN THE BUILDING ENERGY DOMAIN USING OGC STANDARDS

Abstract. As the world has more urbanized, cities need to assess and manage their building energy performances in order to achieve energy-reduction goals. The urban digital twins (UDT) offer promising solutions to this demand by providing valuable insights with qualitative and quantitative information about the building environment. The urban building energy data is not only measured from the equipped sensor devices but can also be simulated based on the analysis software. In this research, we aim to explore the development of the spatial data infrastructure (SDI) for managing building energy in the UDT application by employing the Open Geospatial Consortium (OGC) standards which increases the data usability and efficiency. In our concept SDI, the big data in the UDT application is managed with the OGC specifications as follows: OGC SensorThings API (STA) for data with Spatio-temporal characteristics, OGC API 3D GeoVolumes for 3D geospatial content delivery, OGC CityGML for virtual 3D city models, OGC API Features, Web Feature Service (WFS), and Web Map Service (WMS) for other 2D geospatial contents. This concept enables broad interoperability between multiple data layers and client applications. As a proof of concept, we developed the UDT application called with a highly visual and intuitive user interface using the proposed SDI concept as a part of the EnSysLE project in the study area of three regions in Germany: Ludwigsburg, Dithmarschen, and Ilm-Kreis. The proposed concept can be expanded to other UDT domains and on a larger scale in future work.

Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes.

Simple SummaryThe mosquito is the deadliest animal on earth, transmitting diseases that cause nearly a million deaths and >700 million infections each year. Yet only relatively few mosquito species are “vectors” that transmit diseases. Unfortunately, identifying these vector species is a difficult and time-consuming manual process. A promising and scalable solution involves “citizen science”, whereby the general public provides images of mosquito specimens and breeding habitats using smartphones. However, data from such previous efforts have lacked the necessary integration for a thorough and global understanding of mosquito presence. Here, we standardize and combine data from multiple international citizen science apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to aid researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we used citizen science imagery to develop artificial intelligence software to automatically identify the species and anatomical regions of mosquitoes. Ultimately, we establish a new surveillance system to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps—Mosquito Alert, iNaturalist, and GLOBE Observer’s Mosquito Habitat Mapper and Land Cover—to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

Towards a training data model for artificial intelligence in earth observation

Artificial Intelligence Machine Learning (AI/ML), in particular Deep Learning (DL), is reorienting and transforming Earth Observation (EO). A consistent data model for delivery of training data will support the FAIR data principles (findable, accessible, interoperable, reusable) and enable Web-based use of training data in a spatial data infrastructure (SDI). Existing training datasets, including open source benchmark datasets, are usually packaged into public or personal repositories and lack discoverability and accessibility. Moreover, there is no unified method to describe the training data. Here we propose a training data model for AI in EO to allow documentation, storage, and sharing of geospatial training data in a distributed infrastructure. We present design rationales, information models, and an encoding method. Several scenarios illustrate the intended uses and benefits for EO DL applications in an open Web environment. The relationship with Open Geospatial Consortium (OGC) standards is also discussed, as is the impact on an AI-ready SDI.

VGI-based spatial data infrastructure for land administration

The general public and land administration organizations have long recognized the need to establish and record legal ownership of property and land. To create land ownership records, developing countries have recently begun to public participation methods. For this purpose, a move to the Spatial Data Infrastructure (SDI) to aggregate heterogeneous geospatial information about the property and create interoperability between organizations and stakeholders is unavoidable. Solutions for public participation must also be provided. Designing and developing a model under the SDI policy, based on an open-source architecture, and based on volunteered geographic information (VGI) and citizen-wide participation in collecting rights-based spatial data is the subject of this study. Volunteered Rights-based Spatial Data Infrastructure (VRSDI) is a framework that was proposed and implemented in this study to help with the low-cost, high-speed voluntary collection of land and property legal information and land management in Iran. VRSDI assists people in defining land and property ownership rights, detecting potential disputes, seizures, or conflicts with neighbors, as well as their limits and obligations for land management through rights-based spatial data. VRSDI was conceptualized and implemented using a Software as a Service (SaaS) approach, including solutions for data and service quality control based on the Open Geospatial Consortium (OGC) standards. It benefits from an open-source multi-tier architecture and powerful components, including the AJAX engine, the VGI engine, the service adapter, and the Workflow engine. Also discussed is policymaking for the effective adoption of VGI in a VRSDI and solutions for spatial accuracy measurement and quality control. The findings demonstrated that an approach like this might lead to integrated land management by including citizens and the government.

A Visual Analytics Web Platform for Detecting High Wind Energy Potential in Urban Environments by Employing OGC Standards

Moving into the third decade of the 21st century, smart cities are becoming a vital concept of advancement of the quality of life. Without any doubt, cities today can generate data of high velocity which can be used in plethora of applications. The wind flow inside a city is an area of several studies which span from pedestrian comfort and natural ventilation to wind energy yield. We propose a Visual Analytics platform based on a server-client web architecture capable of identifying areas with high wind energy potential by employing 3D technologies and Open Geospatial Consortium (OGC) standards. The assessment of a whole city or sub-regions will be supported by integrating Computational Fluid Dynamics (CFD) outcomes with historical wind sensor readings. The results, in 3D space, of such analysis could be used by a wide audience, including city planners and citizens, for locating installation points of small-scale horizontal or vertical axis wind turbines in an urban area. A case study in an urban quarter of Stuttgart is used to evaluate the interactiveness of the proposed workflow. The results show an adequate performance, although there is a lot of room for improvement in future work.

IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.

CityThings: An integration of the dynamic sensor data to the 3D city model

Devices from the Internet of Things are being increasingly used in everyday life, and they provide a massive amount of data in various formats. While implementing the Smart Cities initiative, these data are integrated and utilized together with the 3D city model for further applications. Based on Open Geospatial Consortium standards, heterogeneous sensor data can be integrated with the Open Geospatial Consortium SensorThings Application Programming Interface. Similarly, the 3D city model data can be stored and exchanged with the Open Geospatial Consortium CityGML format. However, currently, there is no concrete model to integrate these sensor data with the 3D city model using the Open Geospatial Consortium standards. The existing solution for integrating the sensor data into the 3D city model requires an extension or plug-in for adding the data to the CityGML model. In this paper, we introduce the concept of “CityThings” to integrate dynamic sensor data from the Open Geospatial Consortium SensorThings API into the CityGML 3D city models. We demonstrate the implementation of the CityThings concept in the Smart Villages project in the study area of Wüstenrot, Germany, by integrating dynamic sensor data from several systems including solar panels, agro-thermal plants, and weather monitoring sensors to visualize the sensor data with the 3D city model on the web platform. In the future, this concept can be applied to interconnect dynamic sensor data and 3D city model data in other Smart Cities applications.

Open Standards used in Oceanography Research Spatial Data Repositories in Spain

Spatial data repositories specialised in geographic information systems (GIS) are an extension of map libraries and archives where much of the increase in use and citation for its geo-spatial data comes from making them available through Open Geospatial Consortium (OGC) web services. In this paper, resource needs and teaching perspectives of these open standards will be described and explained as they have become a domain of application in spatial data repositories and, marine data literacy. The research is based on checking ocean research data rules and contexts in Spain within the European obligations as defined by the EU directive INSPIRE. On that premise, Spanish spatial data infrastructures (SDI) are shown integrating OGC Web Services (OWS) with repositories for ocean observation data, a typical kind of big data. The study revealed that the broad European support to the big data open standards (OGC) implementation in the oceanographic community, is conducted with a model suitable for library management systems. However, Spanish participation in European ocean data spaces is limited and a likely explanation is that this question has not been discussed for about a decade. These findings strengthen the links between spatial data repositories and OGC standards, to identify requirements for interoperability work.

Mesoscale Ocean Feature Identification in the North Aegean Sea with the Use of Sentinel-3 Data

The identification of oceanographic circulation related features is a valuable tool for environmental and fishery management authorities, commercial use and institutional research. Remote sensing techniques are suitable for detection, as in situ measurements are prohibitively costly, spatially sparse and infrequent. Still, these imagery applications require a certain level of technical and theoretical skill making them practically unreachable to the immediate beneficiaries. In this paper a new geospatial web service is proposed for providing daily data on mesoscale oceanic feature identification in the North Aegean Sea, produced by Sentinel-3 SLSTR Sea Surface Temperature (SST) imagery, to end users. The service encompasses an automated process for: raw data acquisition, interpolation, oceanic feature extraction and publishing through a webGIS application. Level-2 SST data are interpolated through a Co-Kriging algorithm, involving information from short term historical data, in order to retain as much information as possible. A modified gradient edge detection methodology is then applied to the interpolated products for the mesoscale feature extraction. The resulting datasets are served according to the Open Geospatial Consortium (OGC) standards and are available for visualization, processing and download though a dedicated web portal.

MODULAR APPROACH TO 3D REPRESENTATION OF UNDERGROUND INFRASTRUCTURE IN THE MODEL FOR UNDERGROUND DATA DEFINITION AND INTEGRATION (MUDDI)

Abstract. Data and models of the built environment enable urban systems to serve their inhabitants and adapt to ever increasing rates of change in society, climate, and. Models of the built environment at or above the ground surface need to be 3 dimensional to best fill that role, but the subsurface also has critical 3-dimensional properties that are even more difficult to characterize being hidden from view. There are many compelling use cases for high quality data on the underground environment at varying levels of detail, from which a list of 6 critical use cases are presented here. Data on the location and disposition of buried utility are difficult to collect and maintain, but the value of avoiding damage, delay, injury and cost with good underground data in all these cases far outstrips the cost and difficult of obtaining it. Effective management and utilization of underground data also depend on models and schemas to organize them. Sharing and exchange of such data require standard models that are agreed between data providers and consumers. There are presently a number of applicable models and standards, but they often reflect a specific perspective, focus, and priorities that make it difficult for any one of them to provide a holistic awareness of the entire underground built environment at the multiple levels of complexity required by the use cases. The draft Model for Underground Data Definition and Integration (MUDDI), a comprehensive integration model for underground information takes a modular approach, with a conceptual core that covers basic geometric representations of underground assets, and a number of extension modules that add more specialized capabilities as well as interfaces with existing models. Several prototyping efforts have generated physical implementations of the MUDDI conceptual model and application deployments populated by operational utilities data, in particular the NUAR and LUAR pilot projects sponsored by the UK Geospatial Commission. An Open Geospatial Consortium Standards Working Group (SWG) is being formed to build on the draft MUDDI model as well as the experiences gained in pilot projects, in order to publish a full specification of the model at the conceptual, logical, and physical levels. Another SWG objective will be to create a roadmap of critical extension modules, particularly those which support upcoming 3D-4D digital twin technologies for visualization, operation, and simulation. Other advanced use cases for these extensions, such as mixed reality visualization and navigation, are expected to become common as both the demands on our built environment and the data available to manage it continue to expand.

CRYPTO-SPATIAL: AN OPEN STANDARDS SMART CONTRACTS LIBRARY FOR BUILDING GEOSPATIALLY ENABLED DECENTRALIZED APPLICATIONS ON THE ETHEREUM BLOCKCHAIN

Abstract. Blockchain is an emerging immature technology that disrupt many well established industries nowadays, like finance, supply chain, transportation, energy, official registries (identity, vehicles, …). In this contribution we present a smart contracts library, named Crypto-Spatial, written for the Ethereum Blockchain and designed to serve as a framework for geospatially enabled decentralized applications (dApps) development. The main goal of this work is to investigate the suitability of Blockchain technology for the storage, retrieval and processing of vector geospatial data. The design and the proof-of-concept implementation presented are both based on the Open Geospatial Consortium standards: Simple Feature Access, Discrete Global Grid Systems (DGGS) and Well Known Binary (WKB). Also, the FOAM protocol concept of Crypto-Spatial Coordinate (CSC) was used to uniquely identify spatial features on the Blockchain immutable ledger. The design of the Crypto-Spatial framework was implemented as a set of smart contracts using the Solidity object oriented programming language. The implemented library was assessed toward Etheruem’s best practices design patterns and known security issues (common attacks). Also, a generic architecture for geospatially enabled decentralized applications, combining blockchain and IPFS technologies, was proposed. Finally, a proof-of-concept was developed using the proposed approach which main purpose is to port the UN/FAO-SOLA to Blockchain techspace allowing more transparency and simplifying access to users communities. The smart contracts of this prototype are live on the Rinkeby testnet and the frontend is hosted on Github pages. The source code of the work presented here is available on Github under Apache 2.0 license.

Conversion of Agricultural Land for Urbanization Purposes: A Case Study of the Suburbs of the Capital of Warmia and Mazury, Poland

Population growth, economic globalization and the launch of market economy instruments have become the main triggers for processes related to the anthropogenization of space. According to Organisation for Economic Co-operation and Development (OECD) statistics, the developed area indication tripled in the last 25 years. Humans keep appropriating more natural and semi-natural areas, which entails specific social, economic and environmental consequences. Provisions in some countries’ laws and some economic factors encourage investors to engage in urbanization. The authors of this study noticed a research gap in the analysis of suburban areas in this topic. Our research aimed to analyze the conversion of plots of land used for agricultural purposes into urbanized land in the city’s suburban zone, in areas of high landscape and natural value. We focused on the analysis of geodetic and legal divisions of plots of land and analyzed the conditions of plots of land “ex ante” and “ex post” and the changes in their values. To achieve the research objective, we used Corine Land Cover (CLC) data for various time intervals, orthophotomaps (using the Web Map Service browsing service compliant with Open Geospatial Consortium standards), cadastral data, administrative decisions, data from the real estate market, spatial analyses and statistical modeling (linear, non-linear and stepwise regression). In general, the CLC data resolution enables analysis at regional or national levels. We used them innovatively at the local level because CLC data allowed us to notice the development of the area over time. Detailed research confirmed that, in the studied area, the conversion of agricultural land into developed areas results from economic factors. The division procedure increases the plot value by about 10%. However, the effects of uncontrolled urbanization, which we are currently dealing with, generate long term social and economic losses, difficulties in the labour market and may become a barrier to development.

Development of SOA-based WebGIS framework for education sector

The applications of Geographic Information System (GIS) in the education sector are increasing day by day. The geospatial information can be published, discovered, searched, analyzed, and displayed through webGIS-based applications. Lack of an open source geospatial resource-based platform for data sharing, discovery, and service delivery in the education sector is a critical issue in managing the education of large population in India. The use of open geospatial consortium (OGC) developed open standards for geospatial web services will result in the interoperability of geographic information. In this paper, an interoperable and secure service-oriented architecture (SOA)-based webGIS framework is developed to handle the technical and non-technical issues in the education sector. In this research work, spatial analysis on schools is performed along with the design and development of webGIS framework using SOA, OGC standards, and open source software. The developed webGIS framework, acronym as EduGIS, is interoperable and secure which is implemented for the education sector. The development of webGIS framework is based upon three-tier thin client architecture. The present research work has investigated an optimized adoption of various free and open source software like Quantum GIS, GeoServer, Apache Tomcat, PostGIS, and uDig in different tiers of developed webGIS framework. The interoperability of developed EduGIS ensures that it can be shared across different technologies, data, platforms, and organizations. The development of open source-based webGIS framework will serve as a means of reducing licensing costs in developing countries like India and will promote indigenous technological development for primary education in rural areas.

WEB GIS SOLUTION FOR URBAN PLANNING DOCUMENTATION WORKFLOW

The goal of this work is to study the use of WEB GIS open source solution for workflow organizing of urban planning documentation: zoning permit, building permit, occupancy permit, utilities connection. A web GIS solution Giscuit.Com developed by a moldavian company VEC.MD was used as a framework for implementation of test version of a specialized website Primaria.Giscuit.Com. Giscuit is a cost-effective web mapping platform built on powerful, cutting-edge, open source geospatial components. The goal is to make it as easy as possible to build more secure, reliable and modern web GIS applications. With Giscuit users stay in control of their content through centralized management of vector and raster spatial data. Giscuit allows users to visualize, share, edit and analyze geospatial data. It has powerful web-based administrator panel with features like data import, layer styling, user management, permissions management, publishing data and more. It is compliant with the Open Geospatial Consortium standards, this is achieved through OpenLayers or PHP MapScript supporting several OGC standards like WMS, WFS, WMC etc. Giscuit provides a scalable GIS server platform that can be deployed on a single Linux or Microsoft Windows machine, it can be distributed across multiple servers or deployed on cloud infrastructure.

Land Use versus Land Cover: Geo-Analysis of National Roads and Synchronisation Algorithms

Technological progress in Earth surface observation provides a vast range of information on the land and methods of its use. This enables property owners, users and administrators to monitor the state of the boundaries of the land they own/administer. The land cover, monitored directly on the ground, is not always consistent with the land use entered in the Land and Property Registry (LPR). Discrepancies between these data are often found in former communist countries. One of the reasons for this was the rapid process of land privatisation, which took place in Poland, without updating information on the plot geodetic boundaries. The study examined and compared the land use (entered in the LPR) with the land cover (on the ground) for national roads (acr. LU-LC). The most frequent discrepancies were selected, using CLC2018, digital orthophotomaps (using the Web Map Service (WMS) browsing service compliant with Open Geospatial Consortium (OGC) standards), cadastral data, statistical modelling and an updated survey of the right-of-way. Subsequently, six algorithms were proposed to synchronise the land use and land cover when the right-of-way was used by unauthorised persons, and two algorithms for cases of unauthorised use of land by the road administrator. Currently, it is difficult to synchronise the land cover with the land use from the administrative, legal and social points of view. The results of analyses show that full synchronisation of land use and land cover is complicated and time-consuming, although desired.