Skip to main content
SpringerLink
Log in

Cloud enabled SDI architecture: a review

  • Review Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

With the advancement of GIS technology since its inception in 1960’s, many educational institutions, government departments, public/ private sectors and individuals have started its use for the production and management of spatial data. Spatial Data Infrastructure (SDI) concept was introduced in the early1990’s and provides a set of technologies, standards, protocols, policies and guidelines on the whole cycle of geospatial data production and distributions, i.e., from data capture to storage and to sharing. SDI initiative at national level, termed as National Spatial Data Infrastructure (NSDI), has been taken by different countries including India. Geospatial community is facing various challenges like handling of large volumes of geospatial data, requirement of high computing resources to process geospatial data, scalability and interoperability. Therefore, need of advanced technologies in the form of SDI and cloud computing is realized to resolve the above challenges. Cloud computing has several characteristics like scalability, elasticity and self-provisioning that offers high-performance computing resources to perform geoprocessing efficiently. The main aim of the present paper is to study SDI and its components along with analysis and comparison of NSDI of various countries as well as to conceptualize and discuss service oriented architecture of cloud enabled SDI. Several challenges of the spatial data handling and processing that occurred due to the high intensity of data and lack of processing capability can be solved by adopting proposed cloud enabled SDI architecture. This research will help geospatial community and SDI developers in various perspectives including data sharing and management, interoperability, security and reliability, fault tolerance, mass market solution, upfront cost and global collaboration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ahmad MO, Khan RZ (2015) The cloud computing: a systematic review. International Journal of Innovative Research in Computer and Communication Engineering 3:4066–4075. https://doi.org/10.15680/ijircce.2015.0305086

    Article  Google Scholar 

  • AlHakami H, Aldabbas H, Alwada T (2012) Comparison between cloud and grid computing: review paper. International Journal on Cloud Computing: Services and Architecture 2:1–21. https://doi.org/10.5121/ijccsa.2012.2401

    Article  Google Scholar 

  • ANZLIC (1996) Spatial data infrastructure for Australia and New Zealand. In: ANZLIC - The Spatial Information Council. https://anzlic.gov.au/news/news/spatial-data-infrastructure-australia-and-new-zealand. Accessed 18 Feb 2018

  • Arco E, Boccardo P, Gandino F, et al (2016) An integrated approach for pollution monitoring: smart acquirement and smart information. In: ISPRS annals of the photogrammetry, Remote Sensing and Spatial Information Sciences. ISPRS, Split, Croatia, pp 67–74

  • Armbrust M, Fox A, Griffith R, et al (2009) Above the clouds: a Berkeley view of cloud computing. Technical report no. UCB/EECS-2009-28

  • Bamps C, Beusen P (2004) Spatial data infrastructures in the Netherlands: state of play spring 2004. In: Leuven: spatial applications division, K.U. Leuven Research & Development. http://inspire.ec.europa.eu/reports/stateofplay2004/rcr04NLv7.pdf

  • Baranski B, Schaeffer B, Redweik R (2009) Geoprocessing in the clouds. In: Proceedings of the free and open source software for geospatial (FOSS4G) conference. Sydney, Australia, pp 17–22

  • Baranski B, Deelmann T, Schäffer B (2010) Pay-per-use revenue models for geoprocessing services in the cloud. In: Brovelli MA, Dragicevic S, Li S, Veenendaal B (eds) Proceedings of the first international workshop on pervasive web mapping, Geoprocessing and services (WebMGS 2010). ISPRS, Como, Italy

    Google Scholar 

  • Baranski B, Foerster T, Schäffer B, Lange K (2011) Matching INSPIRE quality of service requirements with hybrid clouds. Transactions in GIS 15:125–142. https://doi.org/10.1111/j.1467-9671.2011.01265.x

    Article  Google Scholar 

  • Behzad B, Padmanabhan A, Liu Y, et al (2011) Integrating CyberGIS Gateway with Windows Azure: A Case Study on MODFLOW Groundwater Simulation. In: Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems. ACM SIGSPATIAL HPDGIS 2011, Chicago, Illinois, pp 26–29

  • Benslimane Y, Plaisent M, Bernard P, Bahli B (2014) Key challenges and opportunities in cloud computing and implications on service requirements: evidence from a systematic literature review. In: 2014 IEEE 6th international conference on cloud computing technology and science. IEEE, Singapore, Singapore, pp 114–121

  • Bernard C, Villanova-oliver M, Gensel J, Le B (2017) Spatio-temporal evolutive data infrastructure: a spatial data infrastructure for managing data flows of territorial statistical information. International Journal of Digital Earth 10:257–273. https://doi.org/10.1080/17538947.2016.1222003

    Article  Google Scholar 

  • Bevelander M, Grothe M (2015) INSPIRE in the Netherlands. https://ies-svn.jrc.ec.europa.eu/attachments/download/930/Netherlands_INSPIRE_implementation_20150211.pdf. Accessed 12 Aug 2019

  • Bhat MA, Shah RM, Ahmad B (2011) Cloud computing: a solution to geographical information systems (GIS). International Journal on Computer Science and Engineering 3:594–600

    Google Scholar 

  • Blower JD (2010) GIS in the cloud: implementing a web map service on Google app engine. In: proceedings of the 1st international conference and exhibition on computing for Geospatial Research & Application, COM.Geo. ACM, Washington, DC, USA

  • Bohm K, Mehler-Bicher A, Fenchel D (2011) GeoVisualAnalytics in the public health sector. In: Proceedings 2011 IEEE international conference on spatial data mining and geographical knowledge services. IEEE, Fuzhou, China, pp 291–294

  • Botts N, Thoms B, Noamani A, Horan TA (2010) Cloud computing architectures for the underserved: public health Cyberinfrastructures through a network of HealthATMs. In: 2010 43rd Hawaii international conference on system sciences. IEEE, Honolulu, HI, USA, pp 1–10

  • Budhathoki NR, Bruce B, Nedovic-Budic Z (2008) Reconceptualizing the role of the user of spatial data infrastructure. GeoJournal 72:149–160. https://doi.org/10.1007/s10708-008-9189-x

  • Bunzel K, Ager A, Schrader-Patton C (2010) Up in the air: Adventures in serving geospatial data using open source software and the cloud. In: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application, COM.Geo’10. ACM, Washington, DC, USA, pp 1–4

  • Buyya R, Yeo CS, Venugopal S et al (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur Gener Comput Syst 25:599–616. https://doi.org/10.1109/CCGRID.2009.97

    Article  Google Scholar 

  • Chappell D (2010) GIS in the cloud. In: The ESRI example. http://www.chappellassoc.com/writing/white_papers/GIS_in_the_Cloud_v1.0%2D%2DChappell.pdf. Accessed 15 Jul 2018

  • Cho G, Crompvoets J (2019) The INSPIRE directive: some observations on the legal framework and implementation. Surv Rev 51:310–317. https://doi.org/10.1080/00396265.2018.1454686

    Article  Google Scholar 

  • Clarke D (2011) Initiatives and challenges of spatial data infrastructure in South Africa. In: PositionIT. http://www.ee.co.za/wp-content/uploads/legacy/posit11/PositionIT_nov-dec11-Pro_33-35.pdf

  • Clinton WJ (1994) coordinating geographic data acquisition and access: the National Spatial Data Infrastructure. In: executive order 12906, Federal Register. Washington, DC

  • Coleman DJ, McLaughlin J (1998) Defining global geospatial data infrastructure (GGDI): components, stakeholders and interfaces. Geomatica 52:129–143

    Google Scholar 

  • Coleman DJ, Rajabifard A, Kolodziej KW (2016) Expanding the SDI environment: comparing current spatial data infrastructure with emerging indoor location-based services. International Journal of Digital Earth 9:629–647. https://doi.org/10.1080/17538947.2015.1119207

    Article  Google Scholar 

  • Conti LA, Fonseca Filho H, Turra A, Amaral ACZ (2018) Building a local spatial data infrastructure (SDI) to collect, manage and deliver coastal information. Ocean Coast Manag 164:136–146. https://doi.org/10.1016/j.ocecoaman.2018.01.034

    Article  Google Scholar 

  • Cook J (2010) A summary view of government cost recovery policies in Australia and New Zealand relating to the supply of public sector information. In: QUT Digital Repository. https://eprints.qut.edu.au/31609/1/c31609.pdf. Accessed 12 Aug 2019

  • Cooper AK, Moellering H, Hjelmager J et al (2012) A spatial data infrastructure model from the computational viewpoint. Int J Geogr Inf Sci 27:1133–1151. https://doi.org/10.1080/13658816.2012.741239

    Article  Google Scholar 

  • Craglia M, Annoni A (2007) INSPIRE: an innovative approach to the development of spatial data infrastructures in Europe. In: Onsrud H (ed) Research and theory in advancing spatial data infrastructure concepts. ESRI Press, Redlands, California, pp 93–106

    Google Scholar 

  • Craglia M, Campagna M (2010) Advanced Regional SDI in Europe: Comparative cost-benefit evaluation and impact assessment perspectives. International Journal of Spatial Data Infrastructures Research 5:145–167. https://doi.org/10.2902/1725-0463.2010.05.art6

    Article  Google Scholar 

  • Craglia M, De Bie K, Jackson D et al (2012) Digital earth 2020: towards the vision for the next decade. International Journal of Digital Earth 5:4–21. https://doi.org/10.1080/17538947.2011.638500

    Article  Google Scholar 

  • Crompvoets J, Bregt A, Rajabifard A, Williamson I (2004) Assessing the worldwide developments of national spatial data clearinghouses. Int J Geogr Inf Sci 18:665–689. https://doi.org/10.1080/13658810410001702030

    Article  Google Scholar 

  • Crompvoets J, Rajabifard A, Van Loenen B, Delgado-Fernández T (2008) Changing demands for spatial data infrastructure assessment: experience from the Netherlands. In: Crompvoets J, Rajabifard A, van Loenen B, Fernández TD (eds) A multi-view framework to assess spatial data Infarastructures. The University of Melbourne, Australia, Melbourne, Digital Print Centre, pp 383–399

    Google Scholar 

  • Czerwinski A, Sandmann S, Stöcker-Meier E, Pluemer L (2007) Sustainable SDI for EU noise mapping in NRW- best practice for INSPIRE. International Journal of Spatial Data Infrastructures Research 2:90–111

    Google Scholar 

  • De Vries WT, Crompvoets J, Stoter J, Vanden Berghe I (2011) Atlas of INSPIRE – Evaluating SDI Development through an Inventory of INSPIRE Experiences of European National. International Journal of Spatial Data Infrastructures Research 6:126–144. https://doi.org/10.2902/1725-0463.2011.06.art6

    Article  Google Scholar 

  • Di L, Chen A, Yang W, Zhao P (2003) The integration of grid technology with OGC web services (OWS) in NWGISS for NASA EOS data. In: proceedings of global grid forum (GGF) 8 and high performance distributed computing (HPDC). Seattle, Washington

  • Díaz L, Remke A, Kauppinen T et al (2012) Future SDI – Impulses from Geoinformatics Research and IT Trends. International Journal of Spatial Data Infrastructures Research 7:378–410. https://doi.org/10.2902/1725-0463.2012.07.art18

    Article  Google Scholar 

  • Dibs H, Shariff ARBM (2013) Elements, issues and challenges in implementation of NSDI. Coordinates 3:42–52

    Google Scholar 

  • Dikaiakos MD, Katsaros D, Mehra P et al (2009) Cloud computing: distributed internet computing for IT and scientific research. IEEE Internet Comput 13:10–13. https://doi.org/10.1007/978-3-642-12636-9

    Article  Google Scholar 

  • Elwood S (2008) Grassroots groups as stakeholders in spatial data infrastructures: challenges and opportunities for local data development and sharing. Int J Geogr Inf Sci 22:71–90. https://doi.org/10.1080/13658810701348971

    Article  Google Scholar 

  • Erwin T, Sweetkind-singer J (2009) The National Geospatial Digital Archive: a collaborative project to archive geospatial data. Journal of Map & Geography Libraries 6:6–25. https://doi.org/10.1080/15420350903432440

    Article  Google Scholar 

  • Esri (2012) Estimating the cost of a GIS in the Amazon™ cloud. In: Esri White Paper. https://www.esri.com/library/whitepapers/pdfs/estimating-cost-gis-cloud.pdf. Accessed 18 Jul 2017

  • European Commission (2007) Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 Establishing an Infrastructure for Spatial Information in the European Community (INSPIRE)

  • European Commission (2016) Report from the commission to the council and the European Parliament on the implementation of directive 2007/2/EC of march 2007 establishing an infrastructure for spatial information in the European Community (INSPIRE) pursuant to article 23. Brussels

  • Evangelidis K, Ntouros K, Makridis S, Papatheodorou C (2014) Geospatial services in the cloud. Comput Geosci 63:116–122. https://doi.org/10.1016/j.cageo.2013.10.007

    Article  Google Scholar 

  • Evangelinos C, Hill CN (2008) Cloud computing for parallel scientific HPC applications: feasibility of running coupled Atmosphere-Ocean climate models on Amazon’s EC2. In: Proceedings of the Cloud Computing and Its Applications Conference. IEEE, Chicago

  • Fernández TD, Lance K, Buck M, Onsrud HJ (2005) Assessing an SDI readiness index. In: Proceedings from the pharaohs to Geoinformatics, FIG working week 2005 and 8th international conference on global spatial data infrastructure. Cairo, Egypt, pp 16–21

  • FGDC (1994) The 1994 plan for the National Spatial Data Infrastructure: building the foundations of an information based society. In: Federal Geographic Data Committee USGS. https://www.fgdc.gov/policyandplanning/NSDI Strategy 1994.pdf. Accessed 2 Mar 2018

  • FGDC (1997) Framework, introduction and guide. In: Book of Federal geographic data committee, Washington. https://www.fgdc.gov/initiatives/framework/handbook. Accessed 25 Apr 2017

  • FGDC (2005) Components of the NSDI. In: Federal Geographic Data Committee USGS. https://www.fgdc.gov/components. Accessed 14 Mar 2017

  • Gandotra I, Abrol P, Gupta P et al (2011) Cloud computing over cluster, grid computing: a comparative analysis. Journal of Grid and Distributed Computing 1:1–4

    Google Scholar 

  • Gao S, Li L, Li W et al (2017) Constructing gazetteers from volunteered big geo-data based on Hadoop. Comput Environ Urban Syst 61:172–186. https://doi.org/10.1016/j.compenvurbsys.2014.02.004

    Article  Google Scholar 

  • Georgiadou Y, Puri SK, Sahay S (2005) The rainbow metaphor: spatial data infrastructure organization and implementation in India. International Studies of Management & Organization 35:48–70. https://doi.org/10.1080/00208825.2005.11043738

    Article  Google Scholar 

  • Geudens T, Macharis C, Crompvoets J, Plastria F (2009) Assessing Spatial Data Infrastructure Policy Strategies Using the Multi-Actor Multi-Criteria Analysis. International Journal of Spatial Data Infrastructures Research 4:265–297. https://doi.org/10.2902/1725-0463.2009.04.art14

    Article  Google Scholar 

  • Giuliani G, Ray N, Lehmann A (2011) Grid-enabled spatial data infrastructure for environmental sciences: challenges and opportunities. Futur Gener Comput Syst 27:292–303. https://doi.org/10.1016/j.future.2010.09.011

    Article  Google Scholar 

  • Goldberg D, Olivares M, Li Z, Klein AG (2014) Maps & GIS data libraries in the era of big data and cloud computing. Journal of Map & Geography Libraries: Advances in Geospatial Information, Collections & Archives 10:100–122. https://doi.org/10.1080/15420353.2014.893944

    Article  Google Scholar 

  • Gong J, Yue P, Zhou H (2010) Geoprocessing in the Microsoft cloud computing platform- azure. In: proceedings the joint symposium of ISPRS technical commission IV & AutoCarto. Orlando, USA, p 6

  • Goodchild MF (1992) Geographical data modeling. Computers & Geosciences 18:401–408. https://doi.org/10.1016/0098-3004(92)90069-4

    Article  Google Scholar 

  • Goodchild MF (2007) Citizens as voluntary sensors: spatial data infrastructure in the world of web 2.0. International Journal of Spatial Data Infrastructures Research 2:24–32

    Google Scholar 

  • Groot R (1997) Spatial data infrastructure (SDI) for sustainable land management. ITC Journal 3:287–294

    Google Scholar 

  • Grus L, van Wijngaarden S, Bregt A, Castelein W (2009) Implementing INSPIRE in the Netherlands. Annals of Geomatics 4:47–46

    Google Scholar 

  • Guan Q, Shi X, Huang M, Lai C (2016) A hybrid parallel cellular automata model for urban growth simulation over GPU/CPU heterogeneous architectures. Int J Geogr Inf Sci 30:494–514. https://doi.org/10.1080/13658816.2015.1039538

    Article  Google Scholar 

  • Harris TM, Lafone HF (2015) Toward an informal spatial data infrastructure: voluntary geographic information, Neogeography, and the role of citizen sensors. In: Cerbova K, Cerba O (eds) SDI, communities and social media. Prague, Czech Republic, Czech Centre for Science and Society

    Google Scholar 

  • Hashemi SM, Bardsiri AK (2012) Cloud computing Vs grid computing. ARPN Journal of Systems and Software 2:188–194

    Google Scholar 

  • Hayakawa YS, Fujita H, Lee S, Sagara T (2016) Challenges and solutions in developing a joint research assist system (JoRAS) using spatial data infrastructure: insights from long-term operations at the Center for Spatial Information Science at the University of Tokyo. International Journal of Spatial Data Infrastructures Research 11:

  • Helmi AM, Farhan MS, Nasr MM (2018) A framework for integrating geospatial information systems and hybrid cloud computing. Comput Electr Eng 67:145–158. https://doi.org/10.1016/j.compeleceng.2018.03.027

    Article  Google Scholar 

  • Hendriks PHJ, Dessers E, van Hootegem G (2012) Reconsidering the definition of a spatial data infrastructure. Int J Geogr Inf Sci 26:1479–1494. https://doi.org/10.1080/13658816.2011.639301

    Article  Google Scholar 

  • Hobona G, Fairbairn D, James P (2014) Workflow enactment of grid-enabled geospatial web services. In: Proceedings of the UK e-Science All Hands Meeting. National e-Science Centre, Nottingham, United Kingdom

  • Holland P, Baker G, Kline KD (2001) Asia Pacific SDI and global map. In: proceedings of international symposium on spatial data infrastructure, University of Melbourne. P 14

  • Huang Q, Yang C, Nebert D, et al (2010) Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon ’ s EC2. In: Proceedings of the ACM SIGSPATIAL international workshop on high performance and distributed geographic information systems. ACM SIGSPATIAL HPDGIS, San Jose, California, pp 35–38

  • Huang T, Gangl ME, Bingham AW (2011) Building climatological services on the cloud. In: proceeding of the 2nd international workshop on software engineering for cloud computing - SECLOUD’11. ACM, Waikiki, Honolulu, HI, USA

  • Huang Q, Li J, Li Z (2017) A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences. International Journal of Digital Earth 11:1753–8947. https://doi.org/10.1080/17538947.2017.1385652

    Article  Google Scholar 

  • Huber M, Mithofer K, Schar P et al (2009) Universal Land Registry to support independent economic development in Tanzania. International Journal of Spatial Data Infrastructures Research 4:52–76. https://doi.org/10.1017/CBO9781107415324.004

    Article  Google Scholar 

  • IDEP (2007) Infraestructura de Datos Espaciales del Perú (IDEP). http://www.geoidep.gob.pe. Accessed 15 Jul 2018

  • INSPIRE (2008) INSPIRE network services architecture. In: Network Services Drafting Team. https://inspire.ec.europa.eu/reports/ImplementingRules/network/D3_5_INSPIRE_NS_Architecture_v3-0.pdf. Accessed 12 Aug 2018

  • INSPIRE Action plan (2016) INSPIRE action plan for BELGIUM 2016-2020

  • ISRO (2001) National Spatial Data Infrastructure (NSDI)- strategy and action plan. Bangalore

  • Jebur MN, Ziaei Z, Tehrany MS et al (2013) A review of recent developments in national spatial data infrastructures (NSDI). Int J Eng Res Appl 3:6–14

    Google Scholar 

  • Jeppesen JH, Ebeid E, Jacobsen RH, Toftegaard TS (2018) Open geospatial infrastructure for data management and analytics in interdisciplinary research. Comput Electron Agric 145:130–141. https://doi.org/10.1016/j.compag.2017.12.026

    Article  Google Scholar 

  • Jiříček Z, Massimo F Di (2011) Microsoft open government data initiative (OGDI), eye on earth case study. In: Hrebicek J, Schimak G, Denzer R (eds) Environmental software systems. Frameworks of eEnvironment. ISESS 2011. IFIP Advances in Information and Communication Technology. Springer-Verlag Berlin Heidelberg, pp 26–32

  • Jourdan P (1998) Spatial development initiatives (SDIs) - the official view. Dev South Afr 15:717–725. https://doi.org/10.1080/03768359808440046

    Article  Google Scholar 

  • Kacamarga MF, Pardamean B, Wijaya H (2015) Lightweight virtualization in cloud computing for research. In: Intan R, Chi C-H, Palit HN, Santoso LW (eds) Intelligence in the era of big data. ICSIIT 2015. Communications in Computer and Information Science. Springer-Verlag Berlin Heidelberg, pp 439–445

  • Kandukuri BR, V RP, Rakshit A (2009) Cloud security issues. In: 2009 IEEE international conference on services computing. IEEE, Bangalore, India, pp 517–520

  • Khajehei K (2014) Role of virtualization in cloud computing. International Journal of Advance Research in Computer Science and Management Studies 2:15–23

    Google Scholar 

  • Kiehle C, Heier C, Greve K (2007) Requirements for next generation spatial data infrastructures-standardized web based Geoprocessing and web service orchestration. Trans GIS 11:819–834. https://doi.org/10.1111/j.1467-9671.2007.01076.x

    Article  Google Scholar 

  • Kim W, Korea S (2009) Cloud computing: today and tomorrow. Journal Of Object Technology Published by ETH Zurich, Chair of Software Engineering 8:65–72

    Google Scholar 

  • Kmoch A, Klug H, Ritchie ABH et al (2016) A spatial data infrastructure approach for the characterization of New Zealand’s groundwater systems. Trans GIS 20:626–641. https://doi.org/10.1111/tgis.12171

    Article  Google Scholar 

  • Kok B, van Loenen B (2005) How to assess the success of National Spatial Data Infrastructures? Comput Environ Urban Syst 29:699–717. https://doi.org/10.1016/j.compenvurbsys.2004.02.001

    Article  Google Scholar 

  • Kouyoumjian V (2010) GIS in the cloud: the new age of cloud computing and geographic information systems. In: ESRI white paper. https://www.esri.com/news/arcwatch/0110/feature.html. Accessed 15 Jul 2017

  • Lachman BE, Wong A, Knopman D, Gavin K (2002) Lessons for the global spatial data infrastructure: international case study analysis. Santa Monica

  • Lance KT, Georgiadou Y, Bregt A (2006) Understanding how and why practitioners evaluate SDI performance. International Journal of Spatial Data Infrastructures Research 1:65–104

    Google Scholar 

  • Lavania KK, Sharma Y, Bakliwal C (2013) A review on cloud computing model. International Journal on Recent and Innovation Trends in Computing and Communication 01:161–163

    Google Scholar 

  • Lee D, Liang SHL (2011) Geopot: a cloud-based geolocation data service for mobile applications. Int J Geogr Inf Sci 25:37–41. https://doi.org/10.1080/13658816.2011.558017

    Article  Google Scholar 

  • Li Q, Zhang T, Yu Y (2011) Using cloud computing to process intensive floating car data for urban traffic surveillance. Int J Geogr Inf Sci 25:1303–1322. https://doi.org/10.1080/13658816.2011.577746

    Article  Google Scholar 

  • Li J, Jiang Y, Yang C et al (2013) Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs). Comput Geosci 59:78–89. https://doi.org/10.1016/j.cageo.2013.04.029

    Article  Google Scholar 

  • Lian JW, Yen DC, Wang YT (2014) An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. Int J Inf Manag 34:28–36. https://doi.org/10.1016/j.ijinfomgt.2013.09.004

    Article  Google Scholar 

  • LINZ (2019) Land Information New Zealand. https://www.linz.govt.nz/. Accessed 12 Aug 2019

  • Liu Y, Sun AY, Nelson K, Hipke WE (2012) Cloud computing for integrated stochastic groundwater uncertainty analysis. International Journal of Digital Earth 6:313–337. https://doi.org/10.1080/17538947.2012.687778

    Article  Google Scholar 

  • Xiaolin Lu (2010) Service and cloud computing oriented web GIS for labor and social security applications. In: The 2nd international conference on information science and engineering. IEEE, Hangzhou, China, pp 4014–4017

  • Luftman J, Zadeh HS (2011) Key information technology and management issues 2010-11: an international study. J Inf Technol 26:193–204. https://doi.org/10.1057/jit.2011.3

    Article  Google Scholar 

  • Makanga P, Smit J (2010) A review of the status of spatial data infrastructure implementation in Africa. South African. Computer Journal 45:18–25. https://doi.org/10.18489/sacj.v45i0.36

    Article  Google Scholar 

  • Mas-Mayoral S, Rodríguez-Pascual AF (2009) National Spatial Data Infrastructure of Spain, Organisational and legal framework. In: proceedings of the 24th international cartographic conference. Santiago, Chile

  • Masser I (2005) GIS worlds- creating spatial data infrastructures. ESRI Press, Redlands

  • Masser I (2010) All shapes and sizes: the first generation of national spatial data infrastructures. Int J Geogr Inf Sci 13:67–84. https://doi.org/10.1080/136588199241463

    Article  Google Scholar 

  • Masser I, Crompvoets J (2016) Qualitative monitoring of information infrastructures: a case study of INSPIRE. Environment and Planning B: Urban Analytics and City Science 45:330–344. https://doi.org/10.1177/0265813516675871

    Article  Google Scholar 

  • Masser I, Rajabifard A, Williamson I (2008) Spatially enabling governments through SDI implementation. Int J Geogr Inf Sci 22:5–20. https://doi.org/10.1080/13658810601177751

    Article  Google Scholar 

  • Massuthe P, Reisig W, Schmidt K (2005) An operating guideline approach to the SOA. Annals of mathematics. Computing & Teleinformatics 1:35–43. https://doi.org/10.18452/2443

  • Mell P, Grance T (2011) The NIST definition of cloud computing (draft). In: National Institute of Standards and Technology special publication 800-145, 7. http://cmapspublic3.ihmc.us/rid=1JZJDN9X4-1MN1YTP-5CC/NIST Def cloud computing SP800-145.Pdf

  • Michener WK, Jones MB (2012) Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology and Evolution 27:88–93. https://doi.org/10.1016/j.tree.2011.11.016

    Article  Google Scholar 

  • Miska M, Kuwahara M (2010) Sustainable management of data driven. In: 13th international IEEE conference on intelligent transportation systems. IEEE, Funchal, Portugal, pp 689–693

  • Mohammadi H, Rajabifard A, Williamson IP (2010) Development of an interoperable tool to facilitate spatial data integration in the context of SDI. Int J Geogr Inf Sci 24:487–505. https://doi.org/10.1080/13658810902881903

    Article  Google Scholar 

  • Naghavi M (2012) Cloud computing as an innovation in GIS & SDI: methodologies, services, issues and deployment techniques. J Geogr Inf Syst 4:597–607. https://doi.org/10.4236/jgis.2012.46062

    Article  Google Scholar 

  • Nebert DD (2004) Developing Spatial Data Infrastructures: The SDI Cookbook Version 2.0

  • Nebert D (2010) Deploying Federal Geospatial Services in the Cloud. http://wiki.esipfed.org/images/2/26/GeoCloudBriefing0111.pdf. Accessed 15 Nov 2018

  • Niaraki AS, Rajabifard A, Kim K, Seo1 J (2010) Ontology based SDI to facilitate spatially enabled society. In: GSDI 12 world conference-realizing spatially enabled societies. GSDI 12, pp 19–22

  • Olfat H, Kalantari M, Rajabifard A et al (2012) Spatial metadata automation: a key to spatially enabling platform. International Journal of Spatial Data Infrastructures Research 7:173–195. https://doi.org/10.2902/ijsdir.v7i0.279

    Article  Google Scholar 

  • Oliveira IL, Filho JL (2015) A spatial data infrastructure review- sorting the actors and Policies from Enterprise viewpoint. In: Hammoudi S, Maciaszek L, Teniente E, et al. (eds) proceedings of the 17th international conference on Enterprise information systems. SciTePress: Setúbal, Portugal, Barcelona, Spain, pp 287–294

  • Omer SMI, Mustafa AB, FAE A (2014) Comparative study between cluster, grid, utility, cloud and autonomic computing. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) 9:61–67

    Article  Google Scholar 

  • Pajorová E, Hluchý L (2011) Visualization tool and its integration in a gateway for astronomy and astrophysics. In: Corchado ES, Snasel V, Sedano J et al (eds) Advances in intelligent and soft computing. Soft Compu. Springer, Berlin, Heidelberg, pp 291–292

    Google Scholar 

  • Pashova L, Bandrova T (2017) A brief overview of current status of European spatial data infrastructures− relevant developments and perspectives for Bulgaria. Geo-Spatial Information Science 20:97–108. https://doi.org/10.1080/10095020.2017.1323524

    Article  Google Scholar 

  • PCM (2006) Infraestructura De Datos Espaciales Del Peru RM 126–2003-PCM. Lima Peru

  • Pineda-zumaran J (2016a) Learning and Knowledge Generation in Local Decision Making in the South: The Case of Urban Infrastructure Provision in Arequipa, Peru. J Plan Educ Res 36:60–75. https://doi.org/10.1177/0739456X15601186

    Article  Google Scholar 

  • Pineda-zumaran J (2016b) Spatial data usage, spatial thinking and spatial knowledge generation: the case of planning practitioners in Arequipa, Peru. Planning Practice & Research 31:270–291. https://doi.org/10.1080/02697459.2016.1158460

    Article  Google Scholar 

  • Pinsonnault R (2013) Toward a user-centric Peru spatial data infrastructure based on free and open source software. Vrije Universiteit Amsterdam, The Netherlands

    Google Scholar 

  • Porcal-gonzalo MC (2015) A strategy for the management, preservation, and reutilization of geographical information based on the lifecycle of geospatial data: an assessment and a proposal based on experiences from Spain and Europe. Journal of Map & Geography Libraries 11:289–329. https://doi.org/10.1080/15420353.2015.1064054

    Article  Google Scholar 

  • Puri SK (2006) Technological frames of stakeholders shaping the SDI implementation: a case study from India. Inf Technol Dev 12:311–331. https://doi.org/10.1002/itdj.20050

    Article  Google Scholar 

  • Rajabifard A (2001) SDI hierarchy from local to global SDI initiatives. Tsukuba, Japan

    Google Scholar 

  • Rajabifard A, Williamson IP (2001) Spatial data Infarstructures: concept, SDI hierarchies and future directions. In: proceedings of GEOMATICS’80 conference. Tehran, Iran, p 10

  • Rajabifard A, Feeney MEF, Williamson IP (2002) Future directions for SDI development. Int J Appl Earth Obs Geoinf 4:11–22. https://doi.org/10.1016/S0303-2434(02)00002-8

    Article  Google Scholar 

  • Rimal BP, Choi E, Lumb I (2010) A taxonomy, survey, and issues of cloud computing ecosystems. In: Antonopoulos N, Gillam L (eds) Cloud computing. Computer Communications and Networks. Springer, London, pp 21–26

    Chapter  Google Scholar 

  • Rodríguez JM (2009) The spatial data infrastructure of Spain as an example of success in Europe. In: Proceedings of Ninth United Nations Regional Cartographic Conference for the Americas. New York

  • SASDI (2018) Draft south African spatial data infrastructure compliance guidelines

  • Schäffer B, Baranski B, Foerster T (2010) Towards spatial data infrastructures in the clouds. In: Painho M, Santos MY, Pundt H (eds) Geospatial thinking. Springer, Berlin, Heidelberg, pp 399–418

    Chapter  Google Scholar 

  • Sharma P (2013) Grid computing Vs cloud computing. International Journal of Information and Computation Technology 3:577–582

    Google Scholar 

  • Sharma S, Godara J (2015) Review paper on cloud computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) 4:58–61

    Google Scholar 

  • Shawish A, Salama M (2014) Cloud computing: paradigms and technologies. In: Xhafa F, Bessis N (eds) Inter-cooperative collective intelligence: techniques and applications. Springer, Berlin, Heidelberg

    Google Scholar 

  • Siebritz LA, Fourie H (2015) The south African spatial data infrastructure: a collaborative SDI. In: Proceedings of Geomatics Indaba, Stream 1. pp 2–10

  • Singh PK (2009) Spatial Data Infrastructure in India: Status, Governance Challenges, and Strategies for Effective Functioning. International Journal of Spatial Data Infrastructures Research 4:359–388. https://doi.org/10.2902/1725-0463.2009.04.art17

    Article  Google Scholar 

  • Sotomayor B, Montero RS, Llorente IM, Foster I (2009) Virtual infrastructure Management in Private and Hybrid Clouds. IEEE Internet Comput 13:14–22. https://doi.org/10.1109/MIC.2009.119

    Article  Google Scholar 

  • Steenis WM (2011) Developing a spatial data infrastructure for use in the military, how to assess progress? The Manchester Metropolitan University

  • Sun A (2013) Environmental Modelling & Software Enabling collaborative decision-making in watershed management using cloud-computing services. Environ Model Softw 41:93–97. https://doi.org/10.1016/j.envsoft.2012.11.008

    Article  Google Scholar 

  • Tan X, Di L, Deng M et al (2016) Agent-as-a-service-based geospatial service aggregation in the cloud: a case study of flood response. Environ Model Softw 84:210–225. https://doi.org/10.1016/j.envsoft.2016.07.001

    Article  Google Scholar 

  • Tang W, Feng W (2017) Parallel map projection of vector-based big spatial data: coupling cloud computing with graphics processing units. Comput Environ Urban Syst 61:187–197. https://doi.org/10.1016/j.compenvurbsys.2014.01.001

    Article  Google Scholar 

  • Tang J, Matyas CJ (2018) Arc4nix: a cross-platform geospatial analytical library for cluster and cloud computing. Comput Geosci 111:159–166. https://doi.org/10.1016/j.cageo.2017.11.011

    Article  Google Scholar 

  • Tang W, Feng W, Jia M et al (2017) A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing. Int J Geogr Inf Sci 31:907–938. https://doi.org/10.1080/13658816.2016.1245419

    Article  Google Scholar 

  • Toomanian A (2012) Methods to improve and evaluate spatial data infrastructures. Lund University, Sweden

    Google Scholar 

  • Vaccari L, Shvaiko P, Marchese M (2009) A geo-service semantic integration in Spatial Data Infrastructures. International Journal of Spatial Data Infrastructures Research 4:24–51. https://doi.org/10.2902/1725-0463.2009.04.art2

    Article  Google Scholar 

  • Vaduva A, Dittrich KR (2001) Metadata management for data warehousing: between vision and reality. In: Proceedings 2001 international database engineering and applications symposium. IEEE, Grenoble, France, France, pp 129–135

  • Vandenbroucke D, Biliouris D (2011a) Spatial data infrastructures in the Netherlands: state of play 2011. In: Leuven: spatial applications division, K.U. Leuven Research & Development https://inspire.ec.europa.eu/reports/stateofplay2011/rcr11NLv123.pdf

  • Vandenbroucke D, Biliouris D (2011b) Spatial data infrastructures in Spain: state of play 2011. In: Leuven: spatial applications division, K.U. Leuven Research & Development. http://inspire.ec.europa.eu/reports/stateofplay2011/rcr11ESv120.pdf

  • Vandenbroucke D, Jansen K, Van Orshoven J (2008) INSPIRE state of play: Genereic approach to assess the status of NSDIs. In: Joep C, Abbas R, Bastiaan van L, Tatiana DF (eds) A multi-view framework to assess SDIs. The Melbourne University Press. Melbourne, pp 145–172

  • Vandenbroucke D, Crompvoets J, Vancauwenberghe G et al (2009) A network perspective on spatial data infrastructures: application to the sub-national SDI of Flanders (Belgium). Trans GIS 13:105–122. https://doi.org/10.1111/j.1467-9671.2009.01166.x

    Article  Google Scholar 

  • Vecchiola C, Chu X, Buyya R (2009) Aneka: A Software Platform for .NET-based Cloud Computing Christian. In: Gentzsch W, Grandinetti L, Joubert G (eds) High speed and large scale scientific computing. IOS Press, Amsterdam, pp 267–295

    Google Scholar 

  • Vouk MA (2008) Cloud computing- issues, research and implementations. J Comput Inf Technol 4:29–34. https://doi.org/10.2498/cit.1001391

    Article  Google Scholar 

  • Wan Z, Hong Y, Khan S et al (2014) A cloud-based global flood disaster community cyber-infrastructure: development and demonstration. Environ Model Softw 58:86–94. https://doi.org/10.1016/j.envsoft.2014.04.007

    Article  Google Scholar 

  • Wang S, Liu Y (2009) TeraGrid GIScience gateway: bridging cyberinfrastructure and GIScience. Int J Geogr Inf Sci 23:631–656. https://doi.org/10.1080/13658810902754977

    Article  Google Scholar 

  • Wang Y, Wang S, Zhou D (2009) Retrieving and indexing spatial data in the cloud computing environment. In: Jaatun MG, Zhao G, Chunming R (eds) Cloud computing. Springer-Verlag, Berlin Heidelberg, Beijing, China, pp 322–331

    Chapter  Google Scholar 

  • Wang L, Ma Y, Yan J et al (2016) pipsCloud: high performance cloud computing for remote sensing big data management and processing. Futur Gener Comput Syst 78:353–368. https://doi.org/10.1016/j.future.2016.06.009

    Article  Google Scholar 

  • Warnest M, Feeney M-E, Rajabifard A, Williamson IP (2002) Fundamental partnerships driving spatial data infrastructure development within Australia. Cartography 31:11–20. https://doi.org/10.1080/00690805.2002.9714202

    Article  Google Scholar 

  • Waters R, Beare M, Walker R, Millot M (2011) Schema Transformation for INSPIRE. International Journal of Spatial Data Infrastructures Research 6:1–22. https://doi.org/10.2902/1725-0463.2011.06.art1

    Article  Google Scholar 

  • Wiemann S, Bernard L, Wiemann S, Bernard L (2016) Spatial data fusion in spatial data infrastructures using linked data linked data. Int J Geogr Inf Sci 30:613–636. https://doi.org/10.1080/13658816.2015.1084420

    Article  Google Scholar 

  • Williamson IP, Rajabifard A, Feeney M-EF (eds) (2003) Developing spatial data infrastructures: from concept to reality. Taylor & Francis, London

    Google Scholar 

  • Wong DWS, Wu CV (1996) Spatial metadata and GIS for decision support. In: Proceedings of HICSS-29: 29th Hawaii international conference on system sciences. IEEE, Wailea, HI, USA, USA, pp 557–566

  • Xia J, Yang C, Liu K et al (2015) Adopting cloud computing to optimize spatial web portals for better performance to support digital earth and other global geospatial initiatives. International Journal of Digital Earth 8:451–475. https://doi.org/10.1080/17538947.2014.929750

    Article  Google Scholar 

  • Xie J, Yang C, Zhou B, Huang Q (2010) High-performance computing for the simulation of dust storms. Comput Environ Urban Syst 34:278–290. https://doi.org/10.1016/j.compenvurbsys.2009.08.002

    Article  Google Scholar 

  • Yang C, Raskin R, Goodchild M, Gahegan M (2010) Geospatial Cyberinfrastructure: past, present and future. Comput Environ Urban Syst 34:264–277. https://doi.org/10.1016/j.compenvurbsys.2010.04.001

    Article  Google Scholar 

  • Yang C, Goodchild M, Huang Q et al (2011) Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? International Journal of Digital Earth 4:305–329. https://doi.org/10.1080/17538947.2011.587547

    Article  Google Scholar 

  • Yang C, Xu Y, Nebert D (2013) Redefining the possibility of digital earth and geosciences with spatial cloud computing. International Journal of Digital Earth 6:297–312. https://doi.org/10.1080/17538947.2013.769783

    Article  Google Scholar 

  • Yu ET, Hawkins A, Kuntz ID et al (2008) The collaborator for MS3D: a new cyber infra structure for the structural elucidation of biological macromolecules and their assemblies using mass spectrometry-based approaches. J Proteome Res 7:4848–4857. https://doi.org/10.1021/pr800443f

    Article  Google Scholar 

  • Yue P, Gong J, Di L et al (2010) GeoPW: laying blocks for the geospatial processing web. Trans GIS 14:755–772. https://doi.org/10.1111/j.1467-9671.2010.01232.x

    Article  Google Scholar 

  • Yue P, Zhou H, Gong J, Hu L (2013) Geoprocessing in cloud computing platforms- a comparative analysis. International Journal of Digital Earth 6:404–425. https://doi.org/10.1080/17538947.2012.748847

    Article  Google Scholar 

  • Yue P, Zhang C, Zhang M et al (2015) An SDI approach for big data analytics: the case on sensor web event detection and Geoprocessing workflow. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8:4720–4728. https://doi.org/10.1109/JSTARS.2015.2494610

    Article  Google Scholar 

  • Zhang Q, Cheng L, Boutaba R (2010) Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications 1:7–18. https://doi.org/10.1007/s13174-010-0007-6

    Article  Google Scholar 

  • Zhang G, Huang Q, Zhu A-X, Keel JH (2016a) Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function. Int J Geogr Inf Sci 30:2230–2252. https://doi.org/10.1080/13658816.2016.1170836

    Article  Google Scholar 

  • Zhang T, Li J, Liu Q, Huang Q (2016b) A cloud-enabled remote visualization tool for time-varying climate data analytics. Environ Model Softw 75:513–518. https://doi.org/10.1016/j.envsoft.2015.10.033

    Article  Google Scholar 

  • Zhang C, Di L, Sun Z et al (2019) Exploring cloud-based web processing service: a case study on the implementation of CMAQ as a service. Environ Model Softw 113:29–41. https://doi.org/10.1016/j.envsoft.2018.11.019

    Article  Google Scholar 

  • Zhoua L, Chen N, Chena Z (2016) Cloud Computing driven Efficient mapping on Soil Moisture under Sensor Web environment. In: 2016 Fifth international conference on agro-Geoinformatics (agro-Geoinformatics). IEEE, Tianjin, China

Download references

Author information

Authors and Affiliations

  1. GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 211004, India

    Ashutosh Kumar Tripathi & Sonam Agrawal

  2. Civil Engineering Department, and GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 211004, India

    R. D. Gupta

Authors
  1. Ashutosh Kumar Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sonam Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. D. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashutosh Kumar Tripathi.

Additional information

Communicated by: H. Babaie

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tripathi, A.K., Agrawal, S. & Gupta, R.D. Cloud enabled SDI architecture: a review. Earth Sci Inform 13, 211–231 (2020). https://doi.org/10.1007/s12145-020-00446-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-020-00446-9

Keywords

Search

Navigation