Relevant bibliographies by topics / Geospatial data sharing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Geospatial data sharing'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Geospatial data sharing"

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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 &amp;ndash; use many times” should apply. It is best to obtain the geospatial data from the authoritative source &amp;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.<br> 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.<br> 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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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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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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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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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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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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Zhai, X., L. Jiang, and P. Yue. "Web-Based Geospatial Resource Sharing Through GeoPW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 131–35. http://dx.doi.org/10.5194/isprsarchives-xl-6-131-2014.

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As Web-related technologies have matured in recent years, an increasing amount of geospatial resources (e.g. geospatial services, workflows, and geospatial data) are available in the distributed Web environment. Consequently, effective and efficient sharing and management of geospatial resources on the Web are necessary for better utilizing these resources for education and scientific research. This matches the vision of Geoprocessing Web, which emphasizes the sharing and access of geoprocessing utilities from the perspectives of communication, collaboration, and participation. Previous work on GeoPW has provided a large number of geoprocessing services over the Web. In this paper, GeoPW goes further to offer a Web platform for sharing geospatial resources. The paper presents the design, implementation, and functions of the platform, which offers a user-friendly environment for publication, discovery, and communication of geospatial data, services, and workflows.
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Rosly, M. A., A. Ahmad, and Z. Tarmidi. "Collaboration for enabling coastal geospatial data sharing: a review." IOP Conference Series: Earth and Environmental Science 169 (July 31, 2018): 012018. http://dx.doi.org/10.1088/1755-1315/169/1/012018.

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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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Cheng, Quanying, Yunqiang Zhu, Hongyun Zeng, Jia Song, Shu Wang, Jinqu Zhang, Lang Qian, and Yanmin Qi. "A Method for Identifying Geospatial Data Sharing Websites by Combining Multi-Source Semantic Information and Machine Learning." Applied Sciences 11, no. 18 (September 18, 2021): 8705. http://dx.doi.org/10.3390/app11188705.

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Geospatial data sharing is an inevitable requirement for scientific and technological innovation and economic and social development decisions in the era of big data. With the development of modern information technology, especially Web 2.0, a large number of geospatial data sharing websites (GDSW) have been developed on the Internet. GDSW is a point of access to geospatial data, which is able to provide a geospatial data inventory. How to precisely identify these data websites is the foundation and prerequisite of sharing and utilizing web geospatial data and is also the main challenge of data sharing at this stage. GDSW identification can be regarded as a binary website classification problem, which can be solved by the current popular machine learning method. However, the websites obtained from the Internet contain a large number of blogs, companies, institutions, etc. If GDSW is directly used as the sample data of machine learning, it will greatly affect the classification precision. For this reason, this paper proposes a method to precisely identify GDSW by combining multi-source semantic information and machine learning. Firstly, based on the keyword set, we used the Baidu search engine to find the websites that may be related to geospatial data in the open web environment. Then, we used the multi-source semantic information of geospatial data content, morphology, sources, and shared websites to filter out a large number of websites that contained geospatial keywords but were not related to geospatial data in the search results through the calculation of comprehensive similarity. Finally, the filtered geospatial data websites were used as the sample data of machine learning, and the GDSWs were identified and evaluated. In this paper, training sets are extracted from the original search data and the data filtered by multi-source semantics, the two datasets are trained by machine learning classification algorithms (KNN, LR, RF, and SVM), and the same test datasets are predicted. The results show that: (1) compared with the four classification algorithms, the classification precision of RF and SVM on the original data is higher than that of the other two algorithms. (2) Taking the data filtered by multi-source semantic information as the sample data for machine learning, the precision of all classification algorithms has been greatly improved. The SVM algorithm has the highest precision among the four classification algorithms. (3) In order to verify the robustness of this method, different initial sample data mentioned above are selected for classification using the same method. The results show that, among the four classification algorithms, the classification precision of SVM is still the highest, which shows that the proposed method is robust and scalable. Therefore, taking the data filtered by multi-source semantic information as the sample data to train through machine learning can effectively improve the classification precision of GDSW, and comparing the four classification algorithms, SVM has the best classification effect. In addition, this method has good robustness, which is of great significance to promote and facilitate the sharing and utilization of open geospatial data.
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Dissertations / Theses on the topic "Geospatial data sharing"

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Lee, Donald C. "Geospatial data sharing in Saudi Arabia." University of Southern Queensland, Faculty of Engineering and Surveying, 2003. http://eprints.usq.edu.au/archive/00001458/.

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This research started with a realization that two organizations in Saudi Arabia were spending large amounts of money, millions of dollars in fact, in acquiring separate sets of geospatial data that had identical basemap components. Both the organizations would be using the data for similar engineering purposes, yet both would be independently outsourcing the data gathering. In all probability, resources are being wasted through two organizations each developing and operating stand-alone geographic information systems and then populating the databases with geospatial data obtained separately. Surely with some cooperation, a shared database could be established, with a diffusion of economic benefits to both organizations. Preliminary discussions with representatives from both the organizations revealed high levels of enthusiasm for the principle of sharing geospatial data, but the discussions also revealed even higher levels of scepticism that such a scheme could be implemented. This dichotomy of views prompted an investigation into the issues, benefits and the barriers involved in data sharing, the relative weight of these issues, and a quest for a workable model. Sharing geospatial data between levels of government, between governmental and private institutions, and within institutions themselves has been attempted on large and small scales in a variety of countries, with varying degrees of accomplishment. Lessons can be learned from these attempts at data sharing, confirming that success is not purely a function of financial and technical benefits, but is also influenced by institutional and cultural aspects. This research is aimed at defining why there is little geospatial data sharing between authorities in Saudi Arabia, and then presenting a workable model as a pilot arrangement. This should take into account issues raised in reference material; issues evidenced through experience in the implementation of systems that were configured as independent structures; issues of culture; and issues apparent in a range of existing data sharing arrangements. The doubts expressed by engineering managers towards using a geospatial database that is shared between institutions in Saudi Arabia have been borne out by the complexity of interrelationships which this research has revealed. Nevertheless, by concentrating on a two party entry level, a model has been presented which shows promise for the implementation of such a scheme. The model was derived empirically and checked against a case study of various other similar ventures, with a consideration of their applicability in the environment of Saudi Arabia. This model follows closely the generic structure of the Singapore Land Hub. The scalability of the model should allow it to be extended to other, multi-lateral data sharing arrangements. An alternative solution could be developed based on a Spatial Data Infrastructure model and this is suggested for ongoing investigation. Major unresolved questions relate to cultural issues, whose depth and intricacy have the potential to influence the realization of successful geospatial data sharing in the Kingdom of Saudi Arabia.
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DeVincenzi, Anthony (Anthony Lawrence). "GeoSense : An open publishing platform for visualization, social sharing, and analysis of geospatial data." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76516.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. [81]-[85]).
by Anthony DeVincenzi.
S.M.
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Evans, Carl Preston. "Development of a geospatial data-sharing method for unmanned vehicles based on the joint architecture for unmanned systems(JAUS)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0009320.

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Books on the topic "Geospatial data sharing"

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Installation Mapping Enables Many Missions: The Benefits of and Barriers to Sharing Geospatial Data Assets. RAND Corporation, 2007.

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1960-, Lachman Beth E., ed. Installation mapping enables many missions: The benefits of and barriers to sharing geospatial data assets. Santa Monica, CA: RAND, 2007.

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Book chapters on the topic "Geospatial data sharing"

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Keller, Stefan F., and Hugo Thalmann. "Modeling and Sharing Graphic Presentations of Geospatial Data." In Interoperating Geographic Information Systems, 151–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10703121_13.

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Caradonna, Grazia, Benedetto Figorito, and Eufemia Tarantino. "Sharing Environmental Geospatial Data Through an Open Source WebGIS." In Computational Science and Its Applications -- ICCSA 2015, 556–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21470-2_40.

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Schade, Sven, Carlos Granell, Glenn Vancauwenberghe, Carsten Keßler, Danny Vandenbroucke, Ian Masser, and Michael Gould. "Geospatial Information Infrastructures." In Manual of Digital Earth, 161–90. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_5.

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Abstract Geospatial information infrastructures (GIIs) provide the technological, semantic, organizational and legal structure that allow for the discovery, sharing, and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial data infrastructures (SDI). We outline the history of GIIs in terms of the organizational and technological developments as well as the current state-of-art, and reflect on some of the central challenges and possible future trajectories. We focus on the tension between increased needs for standardization and the ever-accelerating technological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challenged to become flexible and robust enough to absorb and embrace technological transformations and the accompanying societal and organizational implications. With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments.
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Lanucara, Simone, Salvatore Praticò, and Giuseppe Modica. "Harmonization and Interoperable Sharing of Multi-temporal Geospatial Data of Rural Landscapes." In New Metropolitan Perspectives, 51–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92099-3_7.

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Matin, Mir A., Birendra Bajracharya, and Rajesh Bahadur Thapa. "Lessons and Future Perspectives of Earth Observation and GIT in the HKH." In Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region, 363–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73569-2_19.

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AbstractDuring the last decade, SERVIR has been striving for realizing its vision of “Space to Village” by implementing services that provide innovative solutions to improve livelihoods and foster self-reliance with the help of EO and geospatial technologies. Over these years, there has been significant development in the field of EO and geospatial technology. However, the capacity of the key agencies to utilize these advancements to produce, disseminate, and use information has not been able to catch up with these developments. As cited in the previous chapters, SERVIR-HKH has been working with various partners and stakeholders in co-developing and implementing applied, user-driven EO and geospatial information services in the HKH region. SERVIR-HKH recognizes that the sustainability of information products and applications and their use requires an understanding of users and their needs. Understanding the user’s needs and organizational context is the key to delivering effective services. As illustrated in Chaps. 10.1007/978-3-030-73569-2_2 and 10.1007/978-3-030-73569-2_3, the needs assessment study revealed that the use of geospatial data in the region started in the early 1990s, but there are still gaps in the institutionalization and sharing of that information. Often, individual agencies produce geospatial information for their own purpose and do not share it due to lack of policies. Besides, in most cases, the information would have been generated through specific projects funded by external agencies without proper sustainability planning. And as has happened in many cases, those services could not be continued due to lack of resources and capacity.
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Lemmens, Rob, Gilles Falquet, Chrisa Tsinaraki, Friederike Klan, Sven Schade, Lucy Bastin, Jaume Piera, et al. "A Conceptual Model for Participants and Activities in Citizen Science Projects." In The Science of Citizen Science, 159–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58278-4_9.

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AbstractInterest in the formal representation of citizen science comes from portals, platforms, and catalogues of citizen science projects; scientists using citizen science data for their research; and funding agencies and governments interested in the impact of citizen science initiatives. Having a common understanding and representation of citizen science projects, their participants, and their outcomes is key to enabling seamless knowledge and data sharing. In this chapter, we provide a conceptual model comprised of the core citizen science concepts with which projects and data can be described in a standardised manner, focusing on the description of the participants and their activities. The conceptual model is the outcome of a working group from the COST Action CA15212 Citizen Science to Promote Creativity, Scientific Literacy, and Innovation throughout Europe, established to improve data standardisation and interoperability in citizen science activities. It utilises past models and contributes to current standardisation efforts, such as the Public Participation in Scientific Research (PPSR) Common Conceptual Model and the Open Geospatial Consortium (OGC) standards. Its design is intended to fulfil the needs of different stakeholders, as illustrated by several case studies which demonstrate the model’s applicability.
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Konecny, Milan, Temenoujka Bandrova, Petr Kubicek, Silvia Marinova, Radim Stampach, Zdenek Stachon, and Tomas Reznik. "Digital Earth for Disaster Mitigation." In Manual of Digital Earth, 495–526. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_15.

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Abstract This chapter describes the state-of-the-art of the potential of Digital Earth for progressively better solutions for disaster mitigation. The chapter illustrates the use of strong Digital Earth tools for data sharing and important potential for users, such as 2D or multi-D visualizations. Milestones of developments in early warning, disaster risk management and disaster risk reduction concepts are highlighted as a continuous movement between sustainable development and original concepts of disaster risk reduction. Improved solutions have been based on new research directions formulated in Sustainable Development Goals tasks and by expanding the possibilities of new effective solutions via newly organized data ecosystems generated by the United Nations Global Geospatial Information Management, the Group on Earth Observations and the Group on Earth Observations System of Systems, Copernicus and, more recently, the Digital Belt and Road initiative. The new trends in spatial big data are emphasized; the most important for disaster risk reduction are the basic theses of the U.N. Conference in Sendai. This chapter describes three aspects: innovative Digital Earth development, national and local disaster risk assessment and the benefits arising from the use of maps and dynamic data, and analyses of the contributions of cartography to disaster risk reduction.
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Granell, Carlos, Sven Schade, and Gobe Hobona. "Linked Data." In Advances in Geospatial Technologies, 189–226. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-192-8.ch009.

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A Spatial Data Infrastructure (SDI) is an information infrastructure for enhancing geospatial data sharing and access. At the moment, the service-oriented second generation of SDI is transitioning to a third generation, which is characterized by user-centric approaches. This new movement closes the gap between classical SDI and user contributed content, also known as Volunteered Geographic Information (VGI). Public use and acquisition of information provides additional challenges within and beyond the geospatial domain. Linked Data has been suggested recently as a possible overall solution. This notion refers to a best practice for exposing, sharing, and connecting resources in the (Semantic) Web. This chapter details the Linked Data approach to SDI and suggests it as a possibility to combine SDI with VGI. Thus, a Spatial Linked Data Infrastructure could apply solutions for Linked Data to classical SDI standards. The chapter highlights different implementing strategies, gives examples, and argues for benefits, while at the same time trying to outline possible fallbacks; hopeful this contribution will enlighten a way towards a single shared information space.
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Zhang, Chuanrong, and Weidong Li. "Geospatial Semantic Web for Spatial Data Sharing." In Encyclopedia of Information Science and Technology, Third Edition, 7466–74. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-5888-2.ch735.

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Leipnik, Mark R., and Donald P. Albert. "Interjurisdictional Law Enforcement Data Sharing Issues." In Geographic Information Systems and Crime Analysis, 25–44. IGI Global, 2005. http://dx.doi.org/10.4018/978-1-59140-453-8.ch002.

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This chapter discusses the use of geographic information systems (GIS) to create and disseminate geospatial data among multiple law enforcement agencies in the same metropolitan area, county, region, state and nation. Cooperation between different agencies of government, such as between a municipal police department and a comprehensive-planning, information technology or public works department, with GIS expertise will be discussed. The benefits derived from sharing human and technical resources, from using a common set of geospatial data and a common crime records database schema, and from the centralization of activities, such as geocoding, will be emphasized. Issues impeding interjurisdictional use of GIS, such as technical issues of interoperability, confidentiality concerns and cost-sharing problems, are presented. Multiple examples drawn from the United States and several other countries illustrate the universality of interjurisdictional issues and the value of using GIS to facilitate data sharing and cooperation among multiple law enforcement and government agencies.
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Conference papers on the topic "Geospatial data sharing"

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Valachamy, Mageshwari, Shamsul Sahibuddin, Noor Azurati Ahmad, and Nur Azaliah Abu Bakar. "Geospatial Data Sharing." In ICSCA 2020: 2020 9th International Conference on Software and Computer Applications. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3384544.3384596.

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Frey, Remo Manuel, Thomas Hardjono, Christian Smith, Keeley Erhardt, and Alex 'Sandy' Pentland. "Secure sharing of geospatial wildlife data." In the Fourth International ACM Workshop. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3080546.3080550.

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Mou, Naixia, Lingxian Zhang, Liangjie Yang, and Lihua Zhang. "A new framework of spatial data sharing and interoperability: spatial data memory engine." In Geoinformatics 2006: Geospatial Information Technology, edited by Huayi Wu and Qing Zhu. SPIE, 2006. http://dx.doi.org/10.1117/12.713132.

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Zhao, Jun, Gaohuan Liu, Lit-tao Han, Rui-ju Zhang, and Zhi-an Wang. "Sharing and interoperation of Digital Dongying geospatial data." In Geoinformatics 2006: GNSS and Integrated Geospatial Applications, edited by Deren Li and Linyuan Xia. SPIE, 2006. http://dx.doi.org/10.1117/12.712218.

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Shu, Yanfeng, Jack Fan Zhang, and Xiaofang Zhou. "A Grid-Enabled Architecture for Geospatial Data Sharing." In 2006 IEEE Asia-Pacific Conference on Services Computing. IEEE, 2006. http://dx.doi.org/10.1109/apscc.2006.8.

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Li, Deren, Laixing Liu, and Zhenfeng Shao. "Design and implementation of Wuhan Geospatial Information Sharing Platform." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815913.

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Tateishi, Ryutaro, and Josaphat Tetuko Sri Sumantyo. "Development of geospatial data sharing/overlay system - CEReS Gaia -." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351532.

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Hu, Di, Guonian Lu, Zhaoyuan Yu, Zhuo Zhang, and Jiaying Lu. "A Usage-Centered Approach to Geographical Model Sharing Through the Internet." In 2018 International Workshop on Big Geospatial Data and Data Science (BGDDS). IEEE, 2018. http://dx.doi.org/10.1109/bgdds.2018.8626844.

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Fu, Yingchun, Xiuxiao Yuan, and Lin Gui. "Integration and sharing of spatial data based on spatial information multi-grids." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651140.

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Teng, Longmei, Renyi Liu, and Nan Liu. "A service-oriented heterogeneous geospatial data sharing method in a distributed environment." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.651252.

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Reports on the topic "Geospatial data sharing"

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Ruiz, Marilyn O. Geospatial Data Repository. Sharing Data Across the Organization and Beyond. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada392686.

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Best practices for sharing sensitive environmental geospatial data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2010. http://dx.doi.org/10.4095/288863.

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Journal articles
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