Journal articles: 'Geospatial data'

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Heipke, Christian. "Crowdsourcing geospatial data." ISPRS Journal of Photogrammetry and Remote Sensing 65, no. 6 (November 2010): 550–57. http://dx.doi.org/10.1016/j.isprsjprs.2010.06.005.

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Venkatapuram, Sreeharsha S., and Avinash Gogineni. "Leveraging Open Geospatial Data for Public Visualization." International Journal of Research Publication and Reviews 5, no. 1 (January 24, 2024): 4340–49. http://dx.doi.org/10.55248/gengpi.5.0124.0328.

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He, Lianlian, and Ruixiang Liu. "Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach." ISPRS International Journal of Geo-Information 13, no. 5 (April 28, 2024): 143. http://dx.doi.org/10.3390/ijgi13050143.

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The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.

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Koh, Keumseok, Ayaz Hyder, Yogita Karale, and Maged N. Kamel Boulos. "Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health." Remote Sensing 14, no. 13 (June 23, 2022): 2996. http://dx.doi.org/10.3390/rs14132996.

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Background: Often combined with other traditional and non-traditional types of data, geospatial sensing data have a crucial role in public health studies. We conducted a systematic narrative review to broaden our understanding of the usage of big geospatial sensing, ancillary data, and related spatial data infrastructures in public health studies. Methods: English-written, original research articles published during the last ten years were examined using three leading bibliographic databases (i.e., PubMed, Scopus, and Web of Science) in April 2022. Study quality was assessed by following well-established practices in the literature. Results: A total of thirty-two articles were identified through the literature search. We observed the included studies used various data-driven approaches to make better use of geospatial big data focusing on a range of health and health-related topics. We found the terms ‘big’ geospatial data and geospatial ‘big data’ have been inconsistently used in the existing geospatial sensing studies focusing on public health. We also learned that the existing research made good use of spatial data infrastructures (SDIs) for geospatial sensing data but did not fully use health SDIs for research. Conclusions: This study reiterates the importance of interdisciplinary collaboration as a prerequisite to fully taking advantage of geospatial big data for future public health studies.

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Kay, Sissiel E. "Challenges in sharing of geospatial data by data custodians in South Africa." Proceedings of the ICA 1 (May 16, 2018): 1–6. http://dx.doi.org/10.5194/ica-proc-1-60-2018.

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As most development planning and rendering of public services happens at a place or in a space, geospatial data is required. This geospatial data is best managed through a spatial data infrastructure, which has as a key objective to share geospatial data. The collection and maintenance of geospatial data is expensive and time consuming and so the principle of “collect once &ndash; use many times” should apply. It is best to obtain the geospatial data from the authoritative source &ndash; the appointed data custodian. In South Africa the South African Spatial Data Infrastructure (SASDI) is the means to achieve the requirement for geospatial data sharing. This requires geospatial data sharing to take place between the data custodian and the user. All data custodians are expected to comply with the Spatial Data Infrastructure Act (SDI Act) in terms of geo-spatial data sharing. Currently data custodians are experiencing challenges with regard to the sharing of geospatial data. This research is based on the current ten data themes selected by the Committee for Spatial Information and the organisations identified as the data custodians for these ten data themes. The objectives are to determine whether the identified data custodians comply with the SDI Act with respect to geospatial data sharing, and if not what are the reasons for this. Through an international comparative assessment it then determines if the compliance with the SDI Act is not too onerous on the data custodians. The research concludes that there are challenges with geospatial data sharing in South Africa and that the data custodians only partially comply with the SDI Act in terms of geospatial data sharing. However, it is shown that the South African legislation is not too onerous on the data custodians.

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Mooney, P., and M. Minghini. "GEOSPATIAL DATA EXCHANGE USING BINARY DATA SERIALIZATION APPROACHES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 307–13. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-307-2022.

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Abstract. In this paper we investigate the benefits of binary data serialization as a means of storing and sharing large amounts of geospatial data in an interoperable way. De-facto text-based exchange encodings typically exposed by modern Application Programming Interfaces (APIs), including eXtensible Markup Language (XML) and JavaScript Object Notation (JSON), are generally inefficient for an increasingly higher number of applications due to their inflated volumes of data, low speed and the high computational cost for parsing and processing. In this work we consider comparisons of JSON/Geospatial JSON (GeoJSON) and two popular binary data encodings (Protocol Buffers and Apache Avro) for storing and sharing geospatial data. Using a number of experiments, we illustrate the advantages and disadvantages of both approaches for common workflows that make use of geospatial data encodings such as GeoPackage and GeoJSON. The paper contributes a number of practical recommendations around the potential for binary data serialization for interoperable (geospatial) data storage and sharing in the future.

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Albakri, Maythm. "Development of Spatial Data Infrastructure based on Free Data Integration." Journal of Engineering 21, no. 10 (October 1, 2015): 133–49. http://dx.doi.org/10.31026/j.eng.2015.10.09.

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In recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of how the integration of free geospatial data can be beneficial within domains such as Spatial Data Infrastructures. This was carried out by suggesting a common methodology that uses road networks information such as lengths, centeroids, start and end points, number of nodes and directions to integrate free and open source geospatial datasets. The methodology has been proposed for a particular case study: the use of geospatial data from OpenStreetMap and Google Earth datasets as examples of free data sources. The results revealed possible matching between the roads of OpenStreetMap and Google Earth datasets to serve the development of Spatial Data Infrastructures.

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Plante, Katherine, and Marc Gervais. "Geospatial Data Quality Guarantee." GEOMATICA 69, no. 1 (March 2015): 29–48. http://dx.doi.org/10.5623/cig2015-102.

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Geospatial data has become ubiquitous in our society and abundantly used by public institutions fulfilling their mandates as well as citizen managing their day-to-day affairs. But the dissemination of geospatial data raises certain issues surrounding the nature of the contract involved along with the quality guarantees that may be applicable. Should this data be treated as a tangible or intangible asset? Would the standard guarantees defined by our legislation apply if it were considered intangible? What about the specific characteristics of geospatial data? How simple would it be to guarantee its quality? This article presents an overview of geospatial data quality guarantees under Quebec law. We will first address the intrinsic characteristics of geospatial data, the concepts of quality guarantees and precision, along with implied and conventional guarantees. Next, we will investigate the potential effects of various contract categories on the scope, if not the very existence, of quality guarantees. The results of the analysis hold that a number of quality guarantee variations are possible and that some legal uncertainties remain, which further complicates the dissemination of geospatial data for any organization that seeks to do so.

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Lage, Kathryn. "Cataloging Digital Geospatial Data." Journal of Map & Geography Libraries 3, no. 1 (April 23, 2007): 39–55. http://dx.doi.org/10.1300/j230v03n01_04.

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Sun, Kai, Yunqiang Zhu, Peng Pan, Zhiwei Hou, Dongxu Wang, Weirong Li, and Jia Song. "Geospatial data ontology: the semantic foundation of geospatial data integration and sharing." Big Earth Data 3, no. 3 (July 3, 2019): 269–96. http://dx.doi.org/10.1080/20964471.2019.1661662.

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Bhojani, Shital Hitesh. "Geospatial Data Mining Techniques: Knowledge Discovery in Agricultural." Indian Journal of Applied Research 3, no. 1 (October 1, 2011): 22–24. http://dx.doi.org/10.15373/2249555x/jan2013/10.

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Li, H., W. Huang, Z. Zha, and J. Yang. "APPLICATION AND PLATFORM DESIGN OF GEOSPATIAL BIG DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 293–300. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-293-2021.

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Abstract. With the wide application of Big Data, Artificial Intelligence and Internet of Things in geographic information technology and industry, geospatial big data arises at the historic moment. In addition to the traditional "5V" characteristics of big data, which are Volume, Velocity, Variety, Veracity and Valuable, geospatial big data also has the characteristics of "Location Attribute". At present, the study of geospatial big data are mainly concentrated in: knowledge mining and discovery of geospatial data, Spatiotemporal big data mining, the impact of geospatial big data on visualization, social perception and smart city, geospatial big data services for government decision-making support four aspects. Based on the connotation and extension of geospatial big data, this paper comprehensively defines geospatial big data comprehensively. The application of geospatial big data in location visualization, industrial thematic geographic information comprehensive service and geographic data science and knowledge service is introduced in detail. Furthermore, the key technologies and design indicators of the National Geospatial Big Data Platform are elaborated from the perspectives of infrastructure, functional requirements and non-functional requirements, and the design and application of the National Geospatial Public Service Big Data Platform are illustrated. The challenges and opportunities of geospatial big data are discussed from the perspectives of open resource sharing, management decision support and data security. Finally, the development trend and direction of geospatial big data are summarized and prospected, so as to build a high-quality geospatial big data platform and play a greater role in social public application services and administrative management decision-making.

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PAUL, MANOJ, and S. K. GHOSH. "A SERVICE-ORIENTED APPROACH FOR INTEGRATING HETEROGENEOUS SPATIAL DATA SOURCES REALIZATION OF A VIRTUAL GEO-DATA REPOSITORY." International Journal of Cooperative Information Systems 17, no. 01 (March 2008): 111–53. http://dx.doi.org/10.1142/s0218843008001774.

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Searching and accessing geospatial information in the open and distributed environments of geospatial information systems poses several challenges due to the heterogeneity in geospatial data. Geospatial data is highly heterogeneous — both at the syntactic and semantic level. The requirement for an integration architecture for seamless access of geospatial data has been raised over the past decades. The paper proposes a service-based model for geospatial integration where each geospatial data provider is interfaced on the web as services. The interface for these services has been described with Open Geospatial Consortium (OGC) specified service standards. Catalog service provides service descriptions for the services to be discovered. The semantic of each service description is captured in the form of ontology. The similarity assessment method of request service with candidate services proposed in this paper is aimed at resolving the heterogeneity in semantics of locational terms of service descriptions. In a way, we have proposed an architecture for enterprise geographic information system (E-GIS), which is an organization-wide approach to GIS integration, operation, and management. A query processing mechanism for accessing geospatial information in the service-based distributed environment has also been discussed with the help of a case study.

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Zhang, C., W. Li, and T. Zhao. "Geospatial data sharing based on geospatial semantic web technologies." Journal of Spatial Science 52, no. 2 (December 2007): 35–49. http://dx.doi.org/10.1080/14498596.2007.9635121.

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Janée, Greg. "Preserving Geospatial Data: The National Geospatial Digital Archive's Approach." Archiving Conference 6, no. 1 (January 1, 2009): 25–29. http://dx.doi.org/10.2352/issn.2168-3204.2009.6.1.art00007.

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Breunig, Martin, Patrick Erik Bradley, Markus Jahn, Paul Kuper, Nima Mazroob, Norbert Rösch, Mulhim Al-Doori, Emmanuel Stefanakis, and Mojgan Jadidi. "Geospatial Data Management Research: Progress and Future Directions." ISPRS International Journal of Geo-Information 9, no. 2 (February 4, 2020): 95. http://dx.doi.org/10.3390/ijgi9020095.

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Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis.

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Zhang, Shuai, Manchun Li, Zhenjie Chen, Tao Huang, Sumin Li, Wenbo Li, and Yun Chen. "Parallel Spatial-Data Conversion Engine: Enabling Fast Sharing of Massive Geospatial Data." Symmetry 12, no. 4 (April 1, 2020): 501. http://dx.doi.org/10.3390/sym12040501.

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Large-scale geospatial data have accumulated worldwide in the past decades. However, various data formats often result in a geospatial data sharing problem in the geographical information system community. Despite the various methodologies proposed in the past, geospatial data conversion has always served as a fundamental and efficient way of sharing geospatial data. However, these methodologies are beginning to fail as data increase. This study proposes a parallel spatial data conversion engine (PSCE) with a symmetric mechanism to achieve the efficient sharing of massive geodata by utilizing high-performance computing technology. This engine is designed in an extendable and flexible framework and can customize methods of reading and writing particular spatial data formats. A dynamic task scheduling strategy based on the feature computing index is introduced in the framework to improve load balancing and performance. An experiment is performed to validate the engine framework and performance. In this experiment, geospatial data are stored in the vector spatial data defined in the Chinese Geospatial Data Transfer Format Standard in a parallel file system (Lustre Cluster). Results show that the PSCE has a reliable architecture that can quickly cope with massive spatial datasets.

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Fang, Lanting, Ze Kou, Yulian Yang, and Tao Li. "Representing Spatial Data with Graph Contrastive Learning." Remote Sensing 15, no. 4 (February 5, 2023): 880. http://dx.doi.org/10.3390/rs15040880.

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Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for geospatial data. In this paper, we construct a spatial graph from the locations and propose a geospatial graph contrastive learning method to learn the location representations. Firstly, we propose a skeleton graph in order to preserve the primary structure of the geospatial graph to solve the positioning bias problem of remote sensing. Then, we define a novel mixed node centrality measure and propose four data augmentation methods based on the measure. Finally, we propose a heterogeneous graph attention network to aggregate information from both the structural neighborhood and semantic neighborhood separately. Extensive experiments on both geospatial datasets and non-geospatial datasets are conducted to illustrate that the proposed method outperforms state-of-the-art baselines.

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Kurniawan, Risky, and Teguh Kurniawan. "Comparison of Geospatial Data Management between Indonesia’s One Data and One Map Policy." Jurnal Ad'ministrare 10, no. 1 (March 19, 2023): 39. http://dx.doi.org/10.26858/ja.v10i1.40536.

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Geospatial data is one of the important supports in spatial-based national development. However, there is a problem with the large amount of data overlap. One of the reasons for this data overlap is the difference in the references used. Therefore, a policy is needed regarding the implementation of geospatial data. The Indonesian government has enacted Peraturan Presiden (presidential regulations) of Indonesia’s One Data and One Map Policy. Those policies aim to produce sufficient data. Indonesia's One Data Policy regulates the management of statistical, geospatial, and state financial data at the national level. But, the One Map Policy only regulates the management of geospatial data. As two different policies with the same level, have similar names, and have intersect in the management of geospatial data, there is a potential for misunderstanding in implementing both policies. This article aims to identify the similarities and differences between the two policies. The method is a literature study from various sources. This article used analysis comparison. The findings identified similarities and differences with almost the same amount. Based on the similarities and differences, recommendations are made. The author proposes a synergy of geospatial data management mechanisms or considers deregulate one of the policies after the policy target is completed with further study.

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Pakdil, Mete Ercan, and Rahmi Nurhan Çelik. "Serverless Geospatial Data Processing Workflow System Design." ISPRS International Journal of Geo-Information 11, no. 1 (December 30, 2021): 20. http://dx.doi.org/10.3390/ijgi11010020.

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Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.

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Jozefowicz, Suzanne, Merlin Stone, and Eleni Aravopoulou. "Geospatial data in the UK." Bottom Line 33, no. 1 (October 21, 2019): 27–41. http://dx.doi.org/10.1108/bl-09-2019-0115.

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Purpose The purpose of this paper is to explain the rise of geospatial data, its importance for business and some of the problems associated with its development and use. Design/methodology/approach The paper reviews a certain amount of previously published literature but is based mainly on analysis of the very large number of responses to a consultation paper on geospatial data published by the UK Government. Findings The findings are that while there is strong appreciation of the potential benefits of using geospatial data, there are many barriers to the development, sharing and use of geospatial data, ranging from problems of incompatibility in data definitions and systems to regulatory issues. The implication for governments and for providers and users of geospatial data relates to the need to take a long-term approach to planning in resolving the issues identified. Research limitations/implications The research findings are limited to the UK, but similar findings would be likely in any other large Western country. Practical implications This paper confirms the need for a strong and coherent approach to the planning of geospatial data and systems for the establishment of a clear basis for the different parties to work together and the need to clearly separate the roles of the government in establishing frameworks and standards and the role of the private sector in developing applications and solutions. Social implications Society is increasingly dependent on the use of geospatial data, in improving living standards and dealing with social problems. The recommendations identified in this paper, if followed, will facilitate these improvements. Originality/value The value of this paper is the tight synthesis that it provides of a wide ranging and complex range of responses to the UK Government consultation and placing these responses in the wider context of the development of geospatial data.

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Ahn, D. S., J. H. Park, and J. Y. Lee. "DEFINING GEOSPATIAL DATA FUSION METHODS BASED ON TOPOLOGICAL RELATIONSHIPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (October 30, 2018): 317–19. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-317-2018.

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Abstract. Currently, geospatial datasets are produced in various models and formats in accordance with the spatial scale of the real world such as ground/ surface/underground or indoor/outdoor. The location-based services application also uses the optimal data model and format for each purpose. Therefore, there are various geospatial dataset for representing features of the same space. Various geospatial data on same object cause problems with the financial problems and the suitability of the data. In the paper, we reviewed how to integrate existing geospatial data to utilize geospatial data constructed in different models and formats. There are four main ways to fuse existing geospatial information. The existing geospatial data fusion methods consist of a method through geometry data conversion, a method through the aspect of visualization, a method based on attribute data, and a method using topological relationships. Based on this review, we defined a geospatial data fusion method on topological relationships, which is a method considering topological relationship between geospatial objects. In this method, the topological relationship of objects uses the basic concept of IndoorGML.

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Sanyasi Naidu, Dadi. "Cyber Security Implementation for Application of Geospatial Data." Journal of Switching Hub 8, no. 3 (2023): 1–8. http://dx.doi.org/10.46610/josh.2023.v08i03.001.

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Geospatial information is often seen as just being connected with guides, compasses, and areas. In any case, the application areas of geospatial information are far more extensive and even stretch out to the field of network safety. In addition to the fact that there is a capacity to show focal points and rising network traffic conditions, however, geospatial information likewise can display cybercrime development designs and demonstrate impacted regions as well as the rise of sort of digital dangers. Geospatial information can take care of knowledge frameworks, assist with investigation, and data sharing, and help make situational mindfulness. This is especially helpful in network safety. Geospatial information is exceptionally strong and can serve to meet digital dangers and recognize basic areas of concern. Already, geospatial information was essentially utilized by militaries, insight offices, weather conditions, administrations or traffic lights. At present, the utilization of geospatial information has increased, and it traverses a lot more businesses and areas. So, for network safety, geospatial information has a wide number of purposes. It very well might be hard to track down examples or patterns in huge informational collections. Nonetheless, the realistic capacities of geo-planning assist with introducing information in a more absorbable way. This might assist investigators in recognizing arising issues, dangers, and target regions. In this paper, the helpfulness of geospatial information for network safety is investigated. The paper will cover a structure of the key application regions that geospatial information can serve in the field of network safety.

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Zhang, Ying, Chaopeng Li, Na Chen, Shaowen Liu, Liming Du, Zhuxiao Wang, and Miaomiao Ma. "Semantic Web and Geospatial Unique Features Based Geospatial Data Integration." International Journal on Semantic Web and Information Systems 12, no. 1 (January 2016): 1–22. http://dx.doi.org/10.4018/ijswis.2016010101.

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Since large amount of geospatial data are produced by various sources and stored in incompatible formats, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. First, we provide a uniform integration paradigm for users to retrieve geospatial data. Then, we align the retrieved geospatial data in the modeling process to eliminate heterogeneity with the help of Karma. Our main contribution focuses on addressing the third problem. Previous work has been done by defining a set of semantic rules for performing the linking process. However, the geospatial data has some specific geospatial relationships, which is significant for linking but cannot be solved by the Semantic Web techniques directly. We take advantage of such unique features about geospatial data to implement the linking process. In addition, the previous work will meet a complicated problem when the geospatial data sources are in different languages. In contrast, our proposed linking algorithms are endowed with translation function, which can save the translating cost among all the geospatial sources with different languages. Finally, the geospatial data is integrated by eliminating data redundancy and combining the complementary properties from the linked records. We mainly adopt four kinds of geospatial data sources, namely, OpenStreetMap(OSM), Wikmapia, USGS and EPA, to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

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Heidkamp, C. Patrick, and Jeffrey T. Slomba. "GeoSpatial Sculpture: Nonverbal Communication through the Tactilization of Geospatial Data." GeoHumanities 3, no. 2 (May 2, 2017): 554–66. http://dx.doi.org/10.1080/2373566x.2017.1306425.

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Yang, Yu Bo, Cheng Qi Cheng, and Ji Gang Hao. "Geospatial Data Organization Method Based on GeoSOT Model." Applied Mechanics and Materials 263-266 (December 2012): 1420–23. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1420.

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In order to solve the problem of the hardness of utilization and sharing of geospatial data, a new organization method of mass geospatial data based on global subdivision model was proposed in this study. First, the organization method of mass geospatial data based on GeoSOT was discussed in three aspects. Second, the unified organization framework and system platform was designed. This approach offers an effective way to implement management, organization and use of mass geospatial data.

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Kachanov, Peter, Andrey Zuev, and Konstantin Yatsenko. "Method of overlapping geospatial data." Bulletin of the National Technical University «KhPI» Series: New solutions in modern technologies, no. 12 (1184) (March 31, 2016): 119. http://dx.doi.org/10.20998/2413-4295.2016.12.17.

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Wu, Huayi, Tong Zhang, and Jianya Gong. "GeoComputation for Geospatial Big Data." Transactions in GIS 18 (November 2014): 1–2. http://dx.doi.org/10.1111/tgis.12131.

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Sweetkind, Julie, Mary Lynette Larsgaard, and Tracey Erwin. "Digital Preservation of Geospatial Data." Library Trends 55, no. 2 (2006): 304–14. http://dx.doi.org/10.1353/lib.2006.0065.

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LIU, Zhen, Huadong GUO, and Changlin WANG. "Considerations on Geospatial Big Data." IOP Conference Series: Earth and Environmental Science 46 (November 2016): 012058. http://dx.doi.org/10.1088/1755-1315/46/1/012058.

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Lamont, Melissa. "Managing geospatial data and services." Journal of Academic Librarianship 23, no. 6 (November 1997): 469–73. http://dx.doi.org/10.1016/s0099-1333(97)90171-3.

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Power, E. M., and R. L. Trope. "Acting Responsibly with Geospatial Data." IEEE Security and Privacy Magazine 3, no. 6 (November 2005): 77–80. http://dx.doi.org/10.1109/msp.2005.141.

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Larsgaard, Mary Lynette. "Metaloging of Digital Geospatial Data." Cartographic Journal 42, no. 3 (December 2005): 231–37. http://dx.doi.org/10.1179/000870405x77183.

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Worboys, Michael. "Computation with imprecise geospatial data." Computers, Environment and Urban Systems 22, no. 2 (March 1998): 85–106. http://dx.doi.org/10.1016/s0198-9715(98)00023-4.

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de Zarzà, I., J. de Curtò, and Carlos T. Calafate. "UMAP for Geospatial Data Visualization." Procedia Computer Science 225 (2023): 1661–71. http://dx.doi.org/10.1016/j.procs.2023.10.155.

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Zhai, Z. K., J. W. Liu, J. J. Liu, W. H. Zhao, Y. Gao, and J. Che. "DYNAMIC UPDATING METHOD OF GEOSPATIAL DATABASE WITH INCREMENTAL DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 735–41. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-735-2022.

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Abstract. With the rapid development of computer technology and Geographic Information System (GIS), geospatial data are commonly stored and managed in the geospatial database. And geospatial data are widely used in government decision-making, environmental protection, scientific research, and national defence construction. The frequency of geospatial database updating is getting higher and higher due to economic and social development. In this paper, dynamic updating method of geospatial database with the incremental data is proposed, which means only changed features are updated into the geospatial database. Features that have not changed are not updated. This method can greatly improve the efficiency of database update. It has been applied into dynamic updating of national fundamental geographic information database of China. This database has been updated and published once a year. The current situation of data is improved continuously. It can provide more reliable data guarantee for national economic construction and social development. In the future, there will be further optimization of the dynamic updating method. We also hope more and more geospatial database can be updated by this method.

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Tampubolon, W., and W. Reinhardt. "UAV DATA PROCESSING FOR RAPID MAPPING ACTIVITIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 19, 2015): 371–77. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-371-2015.

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During disaster and emergency situations, geospatial data plays an important role to serve as a framework for decision support system. As one component of basic geospatial data, large scale topographical maps are mandatory in order to enable geospatial analysis within quite a number of societal challenges. The increasing role of geo-information in disaster management nowadays consequently needs to include geospatial aspects on its analysis. Therefore different geospatial datasets can be combined in order to produce reliable geospatial analysis especially in the context of disaster preparedness and emergency response. A very well-known issue in this context is the fast delivery of geospatial relevant data which is expressed by the term “Rapid Mapping”. Unmanned Aerial Vehicle (UAV) is the rising geospatial data platform nowadays that can be attractive for modelling and monitoring the disaster area with a low cost and timely acquisition in such critical period of time. Disaster-related object extraction is of special interest for many applications. In this paper, UAV-borne data has been used for supporting rapid mapping activities in combination with high resolution airborne Interferometric Synthetic Aperture Radar (IFSAR) data. A real disaster instance from 2013 in conjunction with Mount Sinabung eruption, Northern Sumatra, Indonesia, is used as the benchmark test for the rapid mapping activities presented in this paper. On this context, the reliable IFSAR dataset from airborne data acquisition in 2011 has been used as a comparable dataset for accuracy investigation and assessment purpose in 3 D reconstructions. After all, this paper presents a proper geo-referencing and feature extraction method of UAV data to support rapid mapping activities.

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Bereta, K., G. Xiao, and M. Koubarakis. "ANSWERING GEOSPARQL QUERIES OVER RELATIONAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W2 (July 5, 2017): 43–50. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w2-43-2017.

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In this paper we present the system Ontop-spatial that is able to answer GeoSPARQL queries on top of geospatial relational databases, performing on-the-fly GeoSPARQL-to-SQL translation using ontologies and mappings. GeoSPARQL is a geospatial extension of the query language SPARQL standardized by OGC for querying geospatial RDF data. Our approach goes beyond relational databases and covers all data that can have a relational structure even at the logical level. Our purpose is to enable GeoSPARQL querying on-the-fly integrating multiple geospatial sources, without converting and materializing original data as RDF and then storing them in a triple store. This approach is more suitable in the cases where original datasets are stored in large relational databases (or generally in files with relational structure) and/or get frequently updated.

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Amirian, Pouria, and Ali A. Alesheikh. "Publishing Geospatial Data through Geospatial Web Service and XML Database System." American Journal of Applied Sciences 5, no. 10 (October 1, 2008): 1358–68. http://dx.doi.org/10.3844/ajassp.2008.1358.1368.

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Han, Weiguo, Zhengwei Yang, Liping Di, Bei Zhang, and Chunming Peng. "Enhancing Agricultural Geospatial Data Dissemination and Applications Using Geospatial Web Services." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 11 (November 2014): 4539–47. http://dx.doi.org/10.1109/jstars.2014.2315593.

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Yue, Peng, Jianya Gong, and Liping Di. "Augmenting geospatial data provenance through metadata tracking in geospatial service chaining." Computers & Geosciences 36, no. 3 (March 2010): 270–81. http://dx.doi.org/10.1016/j.cageo.2009.09.002.

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Li, Wenwen, Michael F. Goodchild, and Robert Raskin. "Towards geospatial semantic search: exploiting latent semantic relations in geospatial data." International Journal of Digital Earth 7, no. 1 (April 10, 2012): 17–37. http://dx.doi.org/10.1080/17538947.2012.674561.

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Zhu, Yunqiang, and Jie Yang. "Automatic data matching for geospatial models: a new paradigm for geospatial data and models sharing." Annals of GIS 25, no. 4 (October 2, 2019): 283–98. http://dx.doi.org/10.1080/19475683.2019.1670735.

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Kamimura, Kaito. "GSI Maps – Showcase of National Geospatial Data." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-158-2019.

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Abstract. Geospatial Information Authority of Japan (GSI), a special organization of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT), operates web maps "GSI Maps" (https://maps.gsi.go.jp/) (Figure 1). GSI Maps is the showcase of geospatial information developed by GSI and can be used with various devices and web browsers. GSI Maps is one of GSI’s important efforts to realize a society that everyone can get and utilize geospatial information anytime, anywhere.

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Li, Zhenlong, Wenwu Tang, Qunying Huang, Eric Shook, and Qingfeng Guan. "Introduction to Big Data Computing for Geospatial Applications." ISPRS International Journal of Geo-Information 9, no. 8 (August 12, 2020): 487. http://dx.doi.org/10.3390/ijgi9080487.

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The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section.

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Zhang, Chuanrong, and Xinba Li. "Land Use and Land Cover Mapping in the Era of Big Data." Land 11, no. 10 (September 30, 2022): 1692. http://dx.doi.org/10.3390/land11101692.

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We are currently living in the era of big data. The volume of collected or archived geospatial data for land use and land cover (LULC) mapping including remotely sensed satellite imagery and auxiliary geospatial datasets is increasing. Innovative machine learning, deep learning algorithms, and cutting-edge cloud computing have also recently been developed. While new opportunities are provided by these geospatial big data and advanced computer technologies for LULC mapping, challenges also emerge for LULC mapping from using these geospatial big data. This article summarizes the review studies and research progress in remote sensing, machine learning, deep learning, and geospatial big data for LULC mapping since 2015. We identified the opportunities, challenges, and future directions of using geospatial big data for LULC mapping. More research needs to be performed for improved LULC mapping at large scales.

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Zheng, Feng, Han Rong Lu, Yan An, and Le Jiang Guo. "The Research and Implementation of Geospatial Data Management Based on ArcGIS Service." Advanced Materials Research 605-607 (December 2012): 2379–82. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2379.

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The main objective of this paper is to design and develop a basis geospatial data management system. It offers database building, conversion, topology check, query statistics, data extraction, cartographic output, analysis and decision-making for massive geospatial data on a range of basic geospatial data management function. The main research includes the design of basis geospatial database, data management system and the key technologies in system development. Geographic information from 2D to 3D is the result of technical progress and demand-driven, 3D modeling and 3D visualization environment-based spatial analysis and query is the future development direction of the geospatial data management system.

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Zhou, Zhao, Qun Sun, and Xiaohua Lyu. "Research on Event-based Geospatial Data Updating." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-437-2019.

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Abstract. How to update the geospatial data timely and accurately has become the focus of surveying and mapping. However, an efficient updating system has not been set up so far. Still the updating operations depend on human-computer interaction, which is less efficient, labour-consuming and prone to error. Accordingly, this paper proposes a new event-based updating method of geospatial data in order to improve the automation and intelligence of data updating. The main contents of the paper are as follows:On the basis of the five classical categories of spatio-temporal change typology, the paper proposes a new classification which includes create, transform, death, disappear, reappear, split，divide，combine，merge and so on. The above new classification of spatio-temporal change type is more compatible with geospatial data updating.The paper proposes a new conception about the life cycle of geospatial features which simulates spatio-temporal changing process of the geospatial entity on the world. The life cycle of geospatial features is composed of three stages: emergence, existence and death. The rules of the above life cycle of geospatial features are also set up.The paper gives a definition of geographical events and also sets up the conceptual model of geographical eventswhich is composed of time, location, geospatial feature, geographical event type and procedure. To better meet the demand of geospatial data updating, the geographical events are also reclassified into create event, transform event,death event, disappear event, reappear event, evolution event, split event, divide event, combine event and merge event.The paper suggests that homologous geographical feature matching and change detecting should be used to deduce spatio-temporal change type and extract geographical events. The thesis intends to set up an up-to-down matching pattern with four layers so as to improve the efficiency and accuracy of matching. Buffer area matching and attribute matching are used to match the point features of the same names. Both the minimum bounding rectangle overlap area and discrete Fréchet distance are used to match the line features of the same names. As for area features, feature overlapping area and Hausdorff distance are employed in matching. In addition, the thesis proposes to use XML to organize and store dynamic updating operation.Under the guidance of event-based geospatial data updating method, the related prototype system has been established and tested in the updating experiments of residence community and roads.

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Zhao, H. T., W. C. Gao, C. F. Jing, and X. F. Li. "A FULL LIFE DATA QUALITY WORKFLOW RESEARCH AND PROJECT PRACTICE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2021 (June 30, 2021): 327–32. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2021-327-2021.

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Abstract. With the development of technology and geospatial equipment, more and more work, particularly covering a big area survey and mapping work, are conducted with different worker and equipment, or heterogeneous time variation. With the traditional data quality focus on the geospatial data quality itself, which is not include the work files and organization. Facing the heterogeneous characteristics of geospatial data and work organization, a full life data quality workflow was proposed to manage and control geospatial data quality. The proposed workflow is extended to include the file preparation, quality evaluation, and quality calculation phrases. It is demonstrated in one real data quality project, which include vector data, raster data and other geospatial data covering 160 thousands square kilometers and 300 work zones finished by 8 teams. The usability and reliability were validated in our work.

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Coetzee, Serena, Ivana Ivánová, Helena Mitasova, and Maria Brovelli. "Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future." ISPRS International Journal of Geo-Information 9, no. 2 (February 1, 2020): 90. http://dx.doi.org/10.3390/ijgi9020090.

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All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of open source geospatial software, focusing on the Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities, as well as three kinds of open geospatial data (collaboratively contributed, authoritative and scientific). The current state confirms that openness has changed the way in which geospatial data are collected, processed, analyzed, and visualized. A perspective on future developments, informed by responses from professionals in key organizations in the global geospatial community, suggests that open source geospatial software and open geospatial data are likely to have an even more profound impact in the future.

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Journal articles on the topic 'Geospatial data'

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