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

WenWen Li, Xiran Zhou, Sheng Wu

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 SemanticWeb and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.

Original languageEnglish (US)
Article number179
JournalISPRS International Journal of Geo-Information
Volume5
Issue number10
DOIs
StatePublished - Oct 1 2016

Fingerprint

Land use
land cover
land use
Semantics
semantics
software
modeling
ontology
Ontology
topology
deforestation
urbanization
Deforestation
trajectory
decision making
time series
remote sensing
social studies
Time series
Remote sensing

Keywords

  • Change detection
  • Geospatial semantic modeling
  • Land use and land cover
  • Semantic reasoning
  • Spatial reasoning

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

Cite this

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title = "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",
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 SemanticWeb and GIScience communities in terms of the establishment of the (web-based) semantic platform for collaborative question answering and decision-making.",
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