Skip to main content
SpringerLink
Log in

A hierarchical organization approach of multi-dimensional remote sensing data for lightweight Web Map Services

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

Abstract

With the rapid development of the World Wide Web, remote sensing (RS) data have become available to a wider range of public/professional users than ever before. Web Map Services (WMSs) provide a simple Web interface for requesting RS data from distributed geospatial databases. RS data providers typically expect to provide lightweight WMSs. They have a low construction cost, and can be easily managed and deployed on standard hardware/software platforms. However, existing systems for WMSs are often heavyweight and inherently hard to manage, due to their improper usage of databases or data storage. That is, they are not suitable for public data services on the Web. In addition, RS data are moving toward the multi-dimensional paradigm, which is characterized by multi-sensor, multi-spectral, multi-temporal and high resolution. Therefore, an efficient organization and storage approach of multi-dimensional RS data is needed for lightweight WMSs, and the efficient WMSs must support multi-dimensional Web browsing. In this paper, we propose a Global Remote Sensing Data Hierarchical Model (GRHM) based on the image pyramid and tiling techniques. GRHM is a logical model that is independent upon physical storage. To support lightweight WMSs, we propose a physical storage structure, and deploy multi-dimensional RS data on Web servers. To further improve the performance of WMSs, a data declustering method based on Hilbert space-filling curve is adopted for the distributed storage. We also provide an Open Geospatial Consortium (OGC) WMS and a Web map system in Web browsers. Experiments conducted on real RS datasets show promising performance of the proposed lightweight WMSs.

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
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. http://www.adobe.com/products/flex/flex_framework/

  2. http://www.microsoft.com/silverlight/

  3. http://code.google.com/apis/maps/index.html

  4. http://www.jpl.nasa.gov/earth/

  5. http://www.esri.com/software/arcgis/arcgisserver/index.html

  6. http://geoserver.org/

  7. http://mapserver.org/

  8. http://maps.google.com/

  9. http://www.bing.com/maps/

  10. http://worldwind.arc.nasa.gov/

  11. http://www.cresda.com/

  12. http://httpd.apache.org/

References

  • Barclay T, Gray J, Slutz D (2000) Microsoft terraserver: a spatial data warehouse. ACM SIGMOD Record 29(2):307–318

    Article  Google Scholar 

  • Barkstrom B, Hinke T, Gavali S, Smith W, Seufzer W, Hu C, Cordner D (2003) Distributed generation of nasa earth science data products. J Grid Comput 1(2):101–116

    Article  Google Scholar 

  • Beaujardiere J (2006) OpenGIS Web Map Service (WMS) implementation specification. Open Geospatial Consortium Inc., 1st edn

  • Bell D, Kuehnel F, Maxwell C, Kim R, Kasraie K, Gaskins T, Hogan P, Coughlan J (2007) Nasa world wind: opensource gis for mission operations. In: Proceedings of IEEE aerospace conference, IEEE, pp 1–9

  • Boschetti L, Roy D, Justice C (2008) Using nasa’s world wind virtual globe for interactive internet visualization of the global modis burned area product. Int J Remote Sens 29(11):3067–3072

    Article  Google Scholar 

  • Butler D (2006) Virtual globes: the web-wide world. Nature 439(7078):776–778

    Article  Google Scholar 

  • Chang F, Dean J, Ghemawat S, Hsieh W, Wallach D, Burrows M, Chandra T, Fikes A, Gruber R (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst 26(2):1–26

    Article  Google Scholar 

  • Du HC, Sobolewski JS (1982) Disk allocation for cartesian product files on multiple-disk systems. ACM Tran Database Syst 7(1):82–101

    Article  Google Scholar 

  • Faloutsos C, Bhagwat P (1993) Declustering using fractals. In: Proceedings of the second international conference on parallel and distributed information systems, IEEE, pp 18–25

  • Garrett J (2005) Ajax: a new approach to web applications. http://www.adaptivepath.com/publications/essays/archives/000385.php

  • Ghemawat S, Gobioff H, Leung S (2003) The google file system. ACM SIGOPS Oper Syst Rev 37(5):29–43

    Article  Google Scholar 

  • Grafarend EW, Krumm FW (2006) Map projections: cartographic information systems. Springer

  • Iversen S (2008) Large format maps on the internet. In: Peterson MP (ed) International perspectives on maps and the internet, Springer, chap 7, pp 103–114

  • Joan M, Keith P, Nuria J (2009) Candidate OpenGIS web map tiling service implementation standard. Open Geospatial Consortium Inc.

  • Kim MH, Pramanik S (1988) Optimal file distribution for partial match retrieval. ACM SIGMOD Record 17(3):173–182

    Article  Google Scholar 

  • Lam WM, Shapiro JH (1994) A class of fast algorithms for the peano-hilbert space-filling curve. In: Proceedings of IEEE international conference image processing, IEEE, vol 1, pp 638–641

  • Mountrakis G, Agouris P, Stefanidis A (2005) Adaptable user profiles for intelligent geospatial queries. Transactions in GIS 9(4):561–583

    Article  Google Scholar 

  • Oreilly T (2007) What is web 2.0: design patterns and business models for the next generation of software. Commun Strateg 65:17

    Google Scholar 

  • Plesea L (2008) The design, implementation and operation of the jpl onearth wms server. In: Sample J, Shaw K, Tu S, Abdelguerfi M (eds) Geospatial services and applications for the internet, Springer, chap 5, pp 93–109

  • Samet H (1984) The quadtree and related hierarchical data structures. ACM Comput Surv 16(2):187–260

    Article  Google Scholar 

  • Scranton R, Connolly A, Krughoff S, Brewer J, Conti A, Christian C, McLean B, Sosin C, Coombe G, Heckbert P (2007) Sky in google earth: the next frontier in astronomical data discovery and visualization. Arxiv preprint arXiv:07090752

  • Warmerdam F (2008) The geospatial data abstraction library. In: Hall GB, Leahy MG (eds) Open source approaches in spatial data handling, Springer, chap 5, pp 87–104

  • Yang C, Wong D, Yang R, Kafatos M, Li Q (2005) Performance-improving techniques in web-based gis. Int J Geogr Inf Sci 19(3):319–342

    Article  Google Scholar 

  • Yang C, Cao Y, Evans J (2007) Web map server performance and client design principles. Gisci Remote Sens 44(4):320–333

    Article  Google Scholar 

  • Yang C, Goodchild M, Huang Q, Nebert D, Raskin R, Xu Y, Bambacus M, Fay D (2011a) Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing? Int J Digital Earth 4(4):305–329

    Article  Google Scholar 

  • Yang C, Wu H, Huang Q, Li Z, Li J, Li W, Miao L, Sun M (2011b) Webgis performance issues and solutions. In: Li S, Dragicevic S, Veenendaal B (eds) Advances in web-based GIS, mapping services and applications. Taylor & Francis Group, London, pp 121–138

    Chapter  Google Scholar 

Download references

Acknowledgements

Anonymous reviewers provided valuable comments on this manuscript, for which we are very grateful. We would also like to thank Shuai Ma for his contributions in revising this manuscript. This paper has been partly supported by the National Natural Science Foundation of China (NSFC) under Grant No.61003017, and the project of “Remote Sensing Data Integration and Service Platform” from Chinese Academy of Space Technology.

Author information

Authors and Affiliations

  1. National Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Haidian District, Beijing, China

    Yongwang Zhao, Hualei Shen, Dianfu Ma & Yonggang Huang

  2. AVIC Aerospace Life-Support Industries, LTD., NO.29, Xinhua Road, Xiangyang, Hubei, China

    Chunyang Hu

  3. Communication Satellite Division, Chinese Academy of Space Technology, Haidian District, Beijing, China

    Xuan Li

Authors
  1. Yongwang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunyang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hualei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dianfu Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yonggang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongwang Zhao.

Additional information

Communicated by: H. A. Babaie

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhao, Y., Hu, C., Shen, H. et al. A hierarchical organization approach of multi-dimensional remote sensing data for lightweight Web Map Services. Earth Sci Inform 5, 61–75 (2012). https://doi.org/10.1007/s12145-012-0096-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-012-0096-9

Keywords

Search

Navigation