An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study
by
1 School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ 85287-5302, USA
2 School of Computer and Information Science, Southwest University, Chongqing 400715, China
*Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS International Journal of Geo-Information . Oct 2016, Vol. 5, Issue 10, p179. 18p
Abstract:
Evolving Earth observation and change detection techniques enable the automatic identification of Land Use and Land Cover Change (LULCC) over a large extent from massive amounts of remote sensing data. It at the same time poses a major challenge in effective organization, representation and modeling of such information. This study proposes and implements an integrated computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. We first proposed a conceptual model to formally represent the spatiotemporal variation of change data, which is essential knowledge to support various environmental and social studies, such as deforestation and urbanization studies. Then, a spatial ontology was created to encode these semantic spatiotemporal data in a machine-understandable format. Based on the knowledge defined in the ontology and related reasoning rules, a semantic platform was developed to support the semantic query and change trajectory reasoning of areas with LULCC. This semantic platform is innovative, as it integrates semantic and spatial reasoning into a coherent computational and operational software framework to support automated semantic analysis of time series data that can go beyond LULC datasets. In addition, this system scales well as the amount of data increases, validated by a number of experimental results. This work contributes significantly to both the geospatial Semantic Web and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.
Keywords: geospatial semantic modeling; change detection; land use and land cover; semantic reasoning; spatial reasoning.
6. Conclusions
This paper introduces the development and implementation of a computational framework to support the modeling, semantic and spatial reasoning of change information with regard to space, time and topology. The center of the system is a reasoning engine capable of linking existing knowledge and semantic rules to infer dynamically new knowledge for spatial thinking. The knowledge base that the system relies on is encoded in an ontology that models various properties (i.e., image, spatial, temporal) of a single spatial object and the spatial relationships among different objects. Earlier studies of LULCC modeling lack a knowledge-based approach to connect the results of change detection with geoinformation in GIS or spatial databases. Our work integrates both aspects of change information into the proposed ontological model by transforming image, spatial, temporal and thematic semantics in the semantic LULCC model. In addition, this model is not limited to use in LU studies; it is generally applicable for other applications, such as forest management, climate changes and fire monitoring that require extensible use of geographical data. Another important contribution of this work is the proposal of the ways to integrate the machine-understandable ontology, reasoning rules, spatiotemporal data, as well as the inference engine seamlessly into an operational software framework to support on-the-fly query, reasoning and visualization of the change information.
There are several possible extensions of this research. First, we are implementing and integrating the proposed spatial ontology and reasoning framework into an operational cyberinfrastructure framework to support collaborative LULCC querying and decision-making. A prototype has been established [49]. In order to address the computational challenges in handling the management and reasoning of (billion-level) big LULCC data, we are extending the backbone reasoning model to develop a scalable semantic framework that can provide high performance support to spatial indexing, querying and reasoning. Finally, the ontological definition of classes and instances in this work is centered on discrete geospatial objects. This allows us to extend this ontological model to formally describe the geographical scenes that contain both objects and relationships between objects. We believe our work makes a major contribution in terms of providing both conceptual and practical solutions to the semantic modeling and reasoning of change data, and it will greatly benefit the broader Semantic Web and GIScience communities.
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26. Li, W.; Raskin, R.; Goodchild, M.F. Semantic similarity measurement based on knowledge mining: An artificial neural net approach. Int. J. Geogr. Inf. Sci. 2012, 26, 1415–1435. [CrossRef]
27. Sunna, W.; Cruz, I.F. Structure-based methods to enhance geospatial ontology alignment. In GeoSpatial Semantics; Springer: Berlin/Heidelberg, Germany, 2007; pp. 82–97.
28. Goldstone, R.L. Similarity, interactive activation, and mapping. J. Exp. Psychol. Learn. Mem. Cogn. 1994, 20, 3–28. [CrossRef]
29. Lin, D. An information-theoretic definition of similarity. ICML 1998, 98, 296–304.
30. Comber, A.; Fisher, P.; Wadsworth, R. Text mining analysis of land cover semantic overlap. In Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects; CRC Press: Boca Raton, FL, USA, 2015; pp. 197–204. ISPRS Int. J. Geo-Inf. 2016, 5, 179 18 of 18
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37. Batsakis, S.; Petrakis, E.G. Representing temporal knowledge in the semantic web: The extended 4d fluents approach. In Combinations of Intelligent Methods and Applications; Springer: Berlin/Heidelberg, Germany, 2011; pp. 55–69.
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39. McGuinness, D.L.; Van Harmelen, F. OWL web ontology language overview. W3C Recomm. 2004, 10, 1–22.
40. Gutierrez, C.; Hurtado, C.; Vaisman, A. Introducing time into RDF. EEE Trans. Knowl. Data Eng. 2007, 19, 207–218. [CrossRef]
41. Frasincar, F.; Milea, V.; Kaymak, U. tOWL: Integrating time in OWL; Springer: Berlin/Heidelberg, Germany, 2010.
42. Krieger, H.U. A General Methodology for Equipping Ontologies with Time. Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.168.1549&rep=rep1&type=pdf (accessed on 23 June 2016).
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45. Sotirios, B. SOWL: A Framework for Handling Spatio-Temporal Information in OWL. Ph.D. Thesis, Technical University of Crete, Chania, Greece, 2011.
46. Bledsoe, B.P.; Brown, M.C.; Raff, D.A. GeoTools: A toolkit for fluvial system analysis. J. Am. Water Resour. Assoc. 2007, 43, 757–772. [CrossRef]
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49. PolarGlobe. Available online: http://polar.geodacenter.org/lulcc
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