TY - JOUR
T1 - An integrated software framework to support semantic modeling and reasoning of spatiotemporal change of geographical objects
T2 - A use case of land use and land cover change study
AU - Li, WenWen
AU - Zhou, Xiran
AU - Wu, Sheng
N1 - Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/10
Y1 - 2016/10
N2 - 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.
AB - 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.
KW - Change detection
KW - Geospatial semantic modeling
KW - Land use and land cover
KW - Semantic reasoning
KW - Spatial reasoning
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U2 - 10.3390/ijgi5100179
DO - 10.3390/ijgi5100179
M3 - Article
AN - SCOPUS:84994108675
VL - 5
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
SN - 2220-9964
IS - 10
M1 - 179
ER -