Spatial Data Infrastructure Research Articles

Research on New Spatial Data Infrastructure Supports the Circulation of Geographic Information Data Elements

Abstract. Data is an important factor of production in the digital economy era and an important support for building a new development pattern. As an important part of data resources, geographic information data is also an important new factor of production, which plays a unique role in the construction of digital China and economic and social development. From the perspective of releasing the value of data elements, data infrastructure is a new type of infrastructure that provides integrated data aggregation, processing,circulation, application, operation, and security services for society under the support of network, computing power and other facilities. It is an organic whole covering hardware, software, open source protocols, standards, and mechanism design. Spatial data infrastructure (SDI) is a collection of technologies, policies and systems that realize the access and access of spatial data. Through the construction of new spatial data infrastructure (NSDI), it supports the circulation of geographic information data elements,making it an important part of data infrastructure. Firstly, this paper gives the connotation of geographic information data elements circulation. Secondly, it proposed to construct the circulation system of geographic information data elements on the basis of enumerating and summarizing the spatial data infrastructure, in order to better support the construction of digital China and economic and social development. Thirdly, the basic requirements, technical indicators and overall architecture of NSDI are designed. Finally, the opportunities and challenges faced by the circulation of geographic information data elements were summarized, mainly focusing on the aspects of policy mechanisms and security systems. At the end of the paper is a summary and acknowledgments.

From Geospatial Data Cube to AI Cube: the Open Geospatial Engine (OGE) Approach

Abstract. The Earth Observation (EO) analytics are moving from local systems to online cloud computing platforms such as Google Earth Engine (GEE) and Open Geospatial Engine (OGE). A typical approach in existing efforts is to leverage geospatial data cubes with cloud computing to support large-scale big EO data analytics in Digital Earth systems. While online analytical processing (OLAP) can be enabled using the cube approach, it is still not clear how geospatial artificial intelligence (GeoAI) can be incorporated in data cubes to benefit the cube infrastructure. Such an investigation can consolidate the vision of an AI-ready SDI (Spatial Data Infrastructure). The paper presents a systematic approach to incorporate GeoAI models into geospatial data cubes to help create an AI Cube. It covers on-demand model retrieval, cube data and model integration, and distributed model inference. The approach is demonstrated in OGE, which is an EO cloud computing platform layered on the GeoCube implementation. The results show that such an AI Cube enriches a cube infrastructure with GeoAI capabilities, facilitates the on-demand coupling of cube data and GeoAI models, and improves the performance of GeoAI inference.

Spatial Data Infrastructure and Mobile Big Data for Urban Planning Based on the Example of Mikolajki Town in Poland

Spatial Data Infrastructure (SDI) is a decision-making tool that is often used in the area of urban planning. At the same time, many other data sources with great utility potential, such as Big Data, can be identified. The aim of the paper is to present the possibility of using mobile Big Data collections with data from Polish SDI, for the purposes of local spatial planning on the example of the tourist town, Mikolajki in Poland. The publication also focuses on assessing the quality of data, as well as the decision-making process supported by these sources. The draft of the local spatial development plan was verified based on integrated data sources. The results showed that the visualization of Big Data as a heat map may be used in urban tasks and as the thematic layer integrated with vector and raster data sets from the SDI in the geographic information system software. The contribution is the practical example how information about users of mobile devices and some information from behavioral profiles may be analyzed for the purposes of verifying planned land use.

ENGINEERING-GEODESIC EQUIPMENT OF THE TERRITORY USED BY USING GEOINFORMATION SYSTEMS

The purpose of the scientific article is the implementation of an important part of the agricultural spatial data infrastructure, planning of the domestic economy using geographic information systems (GIS). Taking this direction into account, the work examines the following main elements of designing the domestic economy: location of settlements and economic centers; design of internal roads; placement of external engineering networks; use of types of land reclamation for agricultural purposes; allocation of land for the construction of roads of various categories of local importance; placement of agricultural machinery in land use; conduct a technical and economic analysis of the effective use of road junctions and reclamation works as an engineering structure, as well as systematize the engineering and geodetic process necessary to implement these studies based on GIS technologies. With the help of geographic information systems technologies, it will be necessary to determine the following directions of engineering and geodetic equipment of the territory: Information about road transport and roads, Roads in plan, longitudinal and horizontal profiles, Driving through bodies of water and bodies of water on the road, Road surfaces, Road surveys and design of agricultural roads, The basic principle of organizing the construction, maintenance and repair of agricultural roads.

Evaluation of the high-degree global gravity field models in the territory of Kazakhstan

The study presents the results of evaluating the accuracy of the high-degree global gravity field models XGM2019e_2159, SGG-UGM-2, SGG-UGM-1, ЕGM2008, GECO and EIGEN-6C4 in the territory of the Republic of Kazakhstan. Based on the results of the study, the optimal model has been determined which best describes the long-wavelength component of the geoid model of the Republic of Kazakhstan. The results of astronomic measurements made at 59 Laplace stations and the results of the GNSS measurements carried out at 154 high-precision levelling in the framework of an investment project of the National Spatial Data Infrastructure of the Republic of Kazakhstan served as basic information for the study. The study results suggest that the high-degree global geopotential models have an approximately common modelling error, the standard deviation of around 11-12 cm, while the range of deviations in the model values of the height anomalies from those obtained by terrain measurements at the GNSS stations is 68-72 cm, which have a negative average value from –0.097 to –0.123 m. This may indicate a regular drift in the system of normal heights and the difference between the real W0 and the normal U0 of potentials at the beginning of height calculation not being equal to zero. The standard deviation of the component of deflection of the vertical in the plane of meridian Δ in the regions with the altitudes less than 500 m varies from 0.93 to 1.13 arcseconds, in the regions with the altitudes greater than 500 m, from 1.26 to 1.54 arcseconds, while the standard deviation of the component of the deflection of the vertical in the plane of the first vertical Δ in the regions with the altitudes less than 500 m varies from 0.70 to 0.84 arcseconds, in the regions with the altitudes greater than 500 m, from 0.62 to 0.81 arcseconds. Based on the statistical characteristics of the evaluatons of the accuracy of modeling height anomalies obtained from 154 stations of high-precision levelling and on the components of the deflection of the vertical from 59 Laplace stations, the global ultra high-degree geopotential model EIGEN-6C4 has proven to be the optimal global gravity field model which best describes the long-wave component of the geoid model for the Republic of Kazakhstan.

A Review of Pakistan’s National Spatial Data Infrastructure Using Multiple Assessment Frameworks

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI.

Enhancing Quality Control Standards for Armenia's National Spatial Data Infrastructure: A Python-based Approach with Emphasis on Road Spatial Data Layers

Enhancing Quality Control Standards for Armenia's National Spatial Data Infrastructure: A Python-based Approach with Emphasis on Road Spatial Data Layers

Webbing 3D map from UAV images in Da Lat City, Lam Dong Province

Abstract Worldwide, big cities are accelerating to fulfill the establishment of Spatial Data Infrastructure (SDI), which is a key task towards smart city. The mainstay of SDI is to create an important foundation of data capacity to provide necessary and essential information related to the requirements of urban and smart city management. With the advantages of high accuracy, low cost, and flexibility in 3D data creation, UAV (Unmanned Aerial Vehicle) images have been widely applied for the purpose of digital transformation purposes. This article aims to firstly utilize the new opportunities of using UAVs in 3D mapping and sharing 3D map data through the WebGIS platform. Secondly, processing techniques will be described after collecting the UAV images to create orthomosaics, point clouds, and Digital Surface Model (DSM). A classification of ground points was conducted to create a Digital Terrain Model (DTM) in combining with DSM to form a calibrated surface numerical model (Canopy Height Model, CHM). The images map was then digitalized coupled with the object height extracted from the CHM model and assigning attributable data to each object. The results suggested that ArcGIS Online allows users to share information widely, targeting multiple users, and to reduce the cost of GIS software investment in building thematic classes for urban infrastructure management.

Characterizing collaborative mapping projects. A methodological framework for analyzing volunteered geographic information and spatial data infrastructure convergence

Characterizing collaborative mapping projects. A methodological framework for analyzing volunteered geographic information and spatial data infrastructure convergence

An empirical study of the limitations of minimum bounding boxes for defining the extent of geospatial resources: the use of DGGS and other alternatives for improving the performance of spatial searches

A Minimum Bounding Box (MBB) is a rectangle which bounds a geographic feature or dataset. It is commonly used in spatial information systems as a simplified way of describing the spatial extent of a resource. MBBs are typically indexed for searching and discovering resources relevant to a given geographic area of interest. However, this simplification leads to a loss of precision in the description of the extent and can affect the overall precision of the search results. We propose an alternative technique for describing the spatial extent based on the use of DGGS tiles. To measure the precision improvements offered by our method, we designed and implemented an empirical method for evaluating the average precision, and applied it to three different systems: one based on MBB, another on Convex Hull, and ours based on DGGS. The three methods were evaluated with the same test collection obtained from some of the main European geospatial data catalogues compliant with the INSPIRE directive. The results showed that our method outperformed the other two. Where the catalogue average precision of the MBB search scenarios is between 73% and 97%, the DGGS is between 96% and 99%. Additionally, we propose a realistic method of transitioning from the current technologies to the technology we are proposing, considering the current state of the spatial data infrastructures.

OER4SDI: Open Educational Resources for Spatial Data Infrastructures

OER4SDI: Open Educational Resources for Spatial Data Infrastructures

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

An integrated geospatial data model for active travel infrastructure

Active travel has received increased investment and interest in many countries both due to COVID-19 and to policies which promote a shift in mobility behaviours to support a wide range of public and individual goods. However, while there has often been substantial investment in the physical infrastructure that can help facilitate active travel, there has not so far always been commensurate investment in the data infrastructure which can help enable people to shift trips to active modes. Current fragmented data and data models and a lack of data standards pose a barrier to the development of the applications which are needed to support planners, users and journey planning. There is therefore a need for a more integrated, better-connected and more richly attributed active travel geospatial network model. This paper describes the development of such a data model, and its initial application to a case study of Great Britain. It demonstrates how the development, population and maintenance of such a data model could facilitate a range of novel applications, such as the personalisation of active travel journey planning to address different user needs and capabilities. If the potential societal benefits from investment in physical active travel infrastructure are to be fully realised, this needs to be supported by the availability of a robust spatial data infrastructure capable of providing the information required by walkers, wheeled users and cyclists to make effective use of the active travel network.

Spatial data infrastructure components to provide regional climate information services

The assessment of expected climate changes on a regional scale requires appropriate data and information products that must be easily accessible and usable for decision makers. The regional climate information platform ReKIS is a long-established project of three German federal states to address this purpose. However, with increasing content, this web-based data exchange platform lacked in suitable metadata descriptions, search functionalities and interoperability, thus hampering the discovery and access of suitable information. This paper describes the extending and enhancing of ReKIS using state-of-the-art components of Spatial Data Infrastructures (SDI). The components support metadata acquisition, maintenance and publishing using a metadata catalogue. The implementation of a WebGIS server increases interoperability by offering OGC-compliant services and a web framework for knowledge-transfer allows for non-expert access to climate services. The approaches were developed and implemented in a real-world scenario and are suitable for a transfer to other comparable platforms and use cases.

Standards, territory and frontier: environmental policy and the expansion of mining in Minas Gerais

This article aims to debate the environmental policy in the state of Minas Gerais based on observing the standards adopted and the territorial reflexes, highlighting how the relaxing the environmental licensing processes over the past few years has influenced the expansion of the frontier of mining activities to different mesoregions of Minas Gerais. Based on a literature review, documentary research and data available in the System for Consultations and Decisions on Environmental Licensing Processes from the State Secretariat for the Environment and Sustainable Development (SEMAD) and in the Spatial Data Infrastructure (IDE) of the State Environmental System (SISEMA) The text initially characterizes the institutionalization of environmental policy in Minas Gerais, and then analyzes the recent changes in the state’s environmental licensing. Lastly, the territorial implications are indicated by the advance of the frontier of the mineral sector and the corporate mining territories. The analyzes on the changes in licensing and the expansion of the mineral sector in the state cover the period between 2013 and 2022.

A Novel and Extensible Remote Sensing Collaboration Platform: Architecture Design and Prototype Implementation

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps.

ANALYSIS OF THE CURRENT STATE OF THE ISSUE OF CREATING A SPATIAL DATA INFRASTRUCTURE IN THE REPUBLIC OF KAZAKHSTAN

This article is devoted to the analysis of currently existing domestic geoportals with open / free access. The analysis allows to identify the problems and prospects for the development of spatial data infrastructure in the Republic of Kazakhstan, to note the features of geoportals, to show the possibilities of geoportal technologies for solving a variety of management tasks in various fields of activity. The results of the study will be used to implement the integration of a geographic information system and elements of 3D visualization of geological objects.

A Critical Evaluation of Government Role in Spatial Data Infrastructures for Healthcare Decision-Making

This study critically examines the government's pivotal role in promoting and regulating Spatial Data Infrastructures (SDIs), with a special focus on the healthcare sector. Through an in-depth analysis of global practices, our research highlights the indispensable value of SDIs in enhancing healthcare decision-making, emergency management, and public health monitoring by facilitating the integration and analysis of geospatial and healthcare data. Despite the evident benefits, the integration of diverse geospatial data sources poses significant challenges, including issues of data standardization, privacy, and interoperability across various stakeholders. Our findings underscore the balance governments must achieve in promoting SDI growth while ensuring data security, privacy, and open access. The study draws on a comprehensive review of literature and case studies to explore the effectiveness of current government strategies in advancing SDI capabilities, while also addressing the ethical considerations and potential barriers to innovation that stringent regulation might impose. By advocating for a collaborative ecosystem that supports data standardization alongside flexibility for innovation, this paper aims to provide insights into how governments can optimize the utility of spatially enabled data in healthcare and beyond. Our conclusion calls for strategic government action to promote a balanced approach to SDI governance, highlighting the importance of public-private partnerships, robust data security frameworks, and the fostering of an environment conducive to data sharing and innovation. This research contributes to the discourse on the critical intersection of technology, healthcare decision-making, and governance, offering valuable recommendations for policymakers, healthcare professionals, and stakeholders in the digital and geospatial domains.

Cartographic Metadata for Improving Accessibility and Facilitating Knowledge Extraction and Validation in Planetary Mapping Based on Remote-Sensing Observations

The field of planetary mapping and cartography builds almost exclusively on remote-sensing data and can be defined by three distinct concepts: systematic imaging as performed through spacecraft surveying, reference mapping as performed through the compilation of reference maps, i.e., regional to global image and topographic maps, and thematic mapping, which aims at abstracting and contextualizing spatial information to generate complex thematic maps, such as geologic or geomorphologic maps. While thematic mapping represents the highest form of abstraction of information that is provided through systematic mapping, thematic mapping also provides scientific reasoning in support of systematic mapping and exploration through spatially contextualized knowledge. For the development of knowledge, it is paramount to manage and exploit the value of thematic maps as research products, and to design a reliable and transparent development process from the beginning of the mapping phase as there is almost no validation for thematic maps. A key element in accomplishing these objectives is well-designed structures and metadata which are maintained within spatial data infrastructures (SDI) and shared as a coordinated process in research data management through data models. In this contribution, we focus on the need to transfer planetary thematic maps into findable, accessible, interoperable, reusable (FAIR), as well as transparent research data assets to facilitate improved knowledge extraction and also to compensate for limitations caused by the lack of conventional validation options. We review the current status of planetary thematic mapping, and we discuss the principles and roles of mappers and publishers in the process of creating and stewarding digital planetary maps and associated data products. We then present and discuss a set of recommendations that are closely tied to the FAIR concepts in research data management to accomplish such tasks.

Online Decision Support Infrastructures for Integrating Spatial Planning and Flood Risk Management Policies

Accessible geospatial data are crucial for informed decision making and policy development in urban planning, environmental governance, and hazard mitigation. Spatial data infrastructures (SDIs) have been implemented to facilitate such data access. However, with the rapid advancements in geospatial software and modelling tools, it is important to re-visit the theoretical discussion about the different roles of data-focused SDIs and decision support and modelling tools, particularly in relation to their different impacts on policy making and policy integration. This research focuses on addressing this issue within the specific context of policy integration in spatial planning and flood risk management. To investigate this, an experiment was conducted comparing a data-focused SDI, the Myplan Viewer, with a prototype Internet-based Spatially Integrated Policy Infrastructure (SIPI). The findings reveal that the SIPI, which provides access to both data and decision support and modelling tools, significantly enhances policy integration compared to the Myplan Viewer. Moreover, drawing upon communicative action theory, this study underscores that while data-focused SDIs support instrumental goals, they possess limitations in facilitating trade-offs and balancing diverse interests in the policy-making process, particularly in supporting strategic and communicative actions.