Skip to main content
SpringerLink
Log in

Fuzzy spatial relation ontology driven detection of complex geospatial features in a web service environment

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

Abstract

Remote sensing images have been widely used by intelligence analysts to discover geospatial features. The overwhelming volume of remote sensing imagery requires automated methods or systems for feature discovery. Existing research focuses on automatic extraction of isolated or elementary features, such as buildings and roads. It is rather understudied to discover complex geospatial features, which is spatially composed of elementary features. From the e-Science perspective, service computing technologies have shown great promise for widespread automation of data analysis and computation. The discovery of complex features would benefit from service computing technologies by computing spatial relations and their fuzziness among elementary features using geoprocessing services. The discovery process can be automated using an ontology approach. The paper presents how ontologies for complex geospatial features, enriched with fuzzy sets of spatial relations, can automate the workflow generation. Spatial computation functions, fuzzy membership functions, and mathematical fuzzy logical operators, are provided as services, and plugged into workflows on demand to enjoy the benefits of service computing technologies. A prototype system demonstrates on-demand uncertainty-aware detection of complex geospatial features in a geoprocessing service environment.

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

References

  • Arpinar IB, Sheth A, Ramakrishnan C, Usery EL, Azami M, Kwan M (2006) Geospatial ontology development and semantic analytics. Trans GIS 10(4):551–575

    Article  Google Scholar 

  • Arvor D, Durieux L, Andrés S, Laporte MA (2013) Advances in geographic object-based image analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS J Photogramm Remote Sens 82:125–137

    Article  Google Scholar 

  • Awrangjeb M, Ravanbakhsh M, Fraser CS (2010) Automatic detection of residential buildings using LIDAR data and multispectral imagery. ISPRS J Photogramm Remote Sens 65(5):457–467

    Article  Google Scholar 

  • Baltsavias EP (2004) Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems. ISPRS J Photogramm Remote Sens 58(3–4):129–151

    Article  Google Scholar 

  • Blaschke T, Hay GJ, Kelly M, Lang S, Hofmann P, Addink E, Feitosa RQ, van der Meer F, van der Werff H, van Coillie F, Tiede D (2014) Geographic object-based image analysis—towards a new paradigm. ISPRS J Photogramm Remote Sens 87:180–191

    Article  Google Scholar 

  • Bloch I (2005) Fuzzy spatial relationships for image processing and interpretation: a review. Image Vis Comput 23(2):89–110

    Article  Google Scholar 

  • Bobillo F, Straccia U (2011) Fuzzy ontology representation using OWL 2. Int J Approx Reason 52(7):1073–1094

    Article  Google Scholar 

  • Clerici M, Combal B, Pekel JF, Dubois G, van't Klooster J, Skøien JO, Bartholomé E (2014) The eStation, an earth observation processing service in support to ecological monitoring. Ecol Informa 18:162–170

    Article  Google Scholar 

  • Denofsky ME (1976) How near is near? A near specialist. AI Memo No. 344. MIT AI Lab, Cambridge, p 75

    Google Scholar 

  • Egenhofer MJ, Herring J (1990) A mathematical framework for the definition of topological relationships. Proceedings of the Fourth International Symposium on Spatial Data Handling, Columbus, OH, pp 803–813

  • Foster I (2005) Service-oriented science. Science 308(5723):814–817

    Article  Google Scholar 

  • Fox P, Hendler JA (2009) Semantic escience: Encoding meaning in next-generation digitally enhanced science. The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, Redmond, pp 147–152

    Google Scholar 

  • Freire S, Santos T, Navarro A, Soares F, Silva JD, Afonso N, Fonseca A, Tenedorio J (2014) Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery. ISPRS J Photogramm Remote Sens 90:1–9

    Article  Google Scholar 

  • Gleason S, Ferrell R, Cheriyadat A, Vatsavai RR, De S (2010) Semantic information extraction from multispectral geospatial imagery via a flexible framework. In: Proceedings2010 I.E. International Geoscience and Remote Sensing Symposium (IGARSS2010), pp 166–169

  • Gruen A, Kuebler O, Agouris P (1995) Automatic extraction of man-made objects from aerial and space images. Birkhaeuser Verlag, Basel, p 340

    Book  Google Scholar 

  • Herring JR (2011) OpenGIS Implementation Standard for Geographic information - Simple feature access - Part 1: Common architecture, OGC 06-103r4, 93

  • Hey T, Trefethen AE (2005) Cyberinfrastructure for e-Science. Science 308(5723):817–821

    Article  Google Scholar 

  • Hudelot C, Atif J, Bloch I (2008) Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Syst 159(15):1929–1951

    Article  Google Scholar 

  • ISO (2002) ISO 19109: 2002: Geographic information—rules for application schema. International Standards Organisation, Geneva

    Google Scholar 

  • Jensen JR (1996) Introductory digital image processing: a remote sensing perspective. Prentice Hall, Upper Saddle River, pp 8–10

    Google Scholar 

  • Klien E (2007) A rule-based strategy for the semantic annotation of geodata. Trans GIS 11(3):437–452

    Article  Google Scholar 

  • Klien E, Lutz M (2005) The role of spatial relations in automating the semantic annotation of geodata. In: Proceedings of the Conference on Spatial Information Theory (COSIT’05), Ellicottville, New York, pp 133–148

  • Le Ber F, Napoli A (2002) The design of an object-based system for representing and classifying spatial structures and relations. J UCS 8(8):751–773

    Google Scholar 

  • Lüscher P, Weibel R, Burghardt D (2009) Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data. Comput Environ Urban Syst 33(5):363–374

    Article  Google Scholar 

  • Mansourian A, Zoje MJV, Mohammadzadeh A, Farnaghi M (2008) Design and implementation of an on-demand feature extraction web service to facilitate development of spatial data infrastructures. Comput Environ Urban Syst 32(5):377–385

    Article  Google Scholar 

  • Mena JB (2003) State of the artonautomatic road extraction for GISupdate: a novel classification. Pattern Recog Lett 24(16):3037–3058

    Article  Google Scholar 

  • Michaelsen E, Stilla U, Soergel U, Doktorski L (2010) Extraction of building polygons from SAR images: grouping and decision-level in the GESTALT system. Pattern Recog Lett 31(10):1071–1076

    Article  Google Scholar 

  • Nagypál G, Motik B (2003) A fuzzy model for representing uncertain, subjective, and vague temporal knowledge in ontologies. In On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE pp 906–923

  • Naouai M, Hamouda A, Akkari A, Weber C (2011) New approach for road extraction from high resolution remotely sensed images using the quaternionic wavelet. Lect Notes Comput Sci 6669:452–459

    Article  Google Scholar 

  • Perry M, Herring J (2012) OGC GeoSPARQL—A Geographic Query Language for RDF Data. Version 1.0, OGC 11-052r4, Open Geospatial Consortium, Inc

  • Quartulli M, Olaizola IG (2013) A review of EO image information mining. ISPRS J Photogramm Remote Sens 75:11–28

    Article  Google Scholar 

  • Randell DA, Cui Z, Cohn AG (1992) A spatial logic based on regions and connection, In: Proceedings of 3rd International Conference on Knowledge Representation and Reasoning, Morgan Kaufmann, San Mateo, pp 165–176

  • Robinson VB (1990) Interactive machine acquisition of a fuzzy spatial relation. Comput Geosci 16(6):857–872

    Article  Google Scholar 

  • Shariff A, Egenhofer M, Mark D (1998) Natural-language spatial relations between linear and areal objects: the topology and metric of English-language terms. Int J Geogr Inf Sci 12(3):215–246

    Google Scholar 

  • Tatem AJ, Goetz SJ, Hay SI (2008) Fifty years of earth observation satellites. Am Sci 96(5):390–398

    Article  Google Scholar 

  • Tiede D, Huber J, Kienberger S (2012) Implementation of an interactive WebGIS based OBIA geoprocessing service. In: Proceedings International Conference on Geographic Object-Based Image Analysis, 4. (GEOBIA), May 7–9, 2012, Rio de Janeiro, Brazil, pp 402–406

  • Tobin KW, Bhaduri BL, Bright EA, Cheriyadat A, Karnowski TP, Palathingal PJ, Potok TE, Price JR (2006) Automated feature generation in large-scale geospatial libraries for content-based indexing. Photogramm Eng Remote Sens 72(5):531–540

    Article  Google Scholar 

  • Van Zyl TL, Vahed A, McFerren G, Hohls D (2012) Earth observation scientific workflows in a distributed computing environment. Trans GIS 16(2):233–248

    Article  Google Scholar 

  • Varanka DE (2011) Ontology patterns for complex topographic feature types. Cartogr Geogr Inf Sci 38(2):126–136

    Article  Google Scholar 

  • Vatsavai RR, Bhaduri B, Cheriyadat A, Arrowood L, Bright E, Gleason S, Diegert C, Katsaggelos A, Pappas T, Porter R, Bollinger J, Chen B, Hohimer R (2010a) Geospatial image mining for nuclear proliferation detection: Challenges and new opportunities. In: Proceedings 2010 I.E. International Geoscience and Remote Sensing Symposium (IGARSS2010), pp 48–51

  • Vatsavai RR, Cheriyadat A, Gleason S (2010b) Unsupervised semantic labeling framework for identification of complex facilities in high-resolution remote sensing images. In: Proceedings 2010 I.E. International Conference on Data Mining Workshops (ICDMW), Sydney, Australia, pp 273–280

  • Yang B, Luan X, Li Q (2010) An adaptive method for identifying the spatial patterns in road networks. Comput Environ Urban Syst 34:40–48

    Article  Google Scholar 

  • Yue P, Gong J, Di L, Yuan J, Sun L, Sun Z, Wang Q (2010) GeoPW: laying blocks for the geospatial processing web. Trans GIS 14(6):755–772

    Article  Google Scholar 

  • Yue P, Di L, Han W, Zhao P, Yang W, He L (2012) Service-oriented approach for geospatial feature discovery. Earth Sci Informa 5(3–4):153–165

    Article  Google Scholar 

  • Yue P, Di L, Wei Y, Han W (2013) Intelligent services for discovery of complex geospatial features from remote sensing imagery. ISPRS J Photogramm Remote Sens 83:151–164

    Article  Google Scholar 

  • Zhao P, Foerster T, Yue P (2012) The geoprocessing web. Comput Geosci 47(10):3–12

    Article  Google Scholar 

  • Zimmermann HJ (2001) Fuzzy sets theory and its application, Kluwer Academic Publishers, 4th ed

Download references

Acknowledgments

We thank anonymous reviewers for constructive comments and suggestions. The work was supported by National Basic Research Program of China (2011CB707105), National Natural Science Foundation of China (41271397), Program for New Century Excellent Talents in University (NCET-13-0435), Key Laboratory of Poyang Lake Wetland and Watershed Research Jiangxi Normal University Ministry of Education (JXS-EW-08), and Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Wuhan University, Wuhan, China

    Lianlian He

  2. Department of Mathematics, Hubei University of Education, Hubei, China

    Lianlian He

  3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

    Peng Yue, Liangcun Jiang & Mingda Zhang

  4. Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang, 330022, China

    Peng Yue

Authors
  1. Lianlian He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peng Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liangcun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mingda Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianlian He.

Additional information

Communicated by: H. A. Babaie

Published in the Special Issue "Semantic e-Science" with Guest Editors Dr. Xiaogang Ma, Dr. Peter Fox, Dr. Thomas Narock and Dr. Brian Wilson

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, L., Yue, P., Jiang, L. et al. Fuzzy spatial relation ontology driven detection of complex geospatial features in a web service environment. Earth Sci Inform 8, 63–76 (2015). https://doi.org/10.1007/s12145-014-0186-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-014-0186-y

Keywords

Search

Navigation