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
2016 ISPRS International Journal of Geo-Information  
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
more » ... 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. These challenges stem from the fact that change in one or more dimensions can cause a spatial object to split into multiple objects, merge with another spatial object(s) or dissolve into a new object. Furthermore, changes that combine multiple relationships (dimensions) such as these are very difficult to model. Figure 1 illustrates three challenges facing any semantic model for change information modeling from time series remote sensing data. These challenges stem from the fact that change in one or more dimensions can cause a spatial object to split into multiple objects, merge with another spatial object(s) or dissolve into a new object. Furthermore, changes that combine multiple relationships (dimensions) such as these are very difficult to model. Figure 1 illustrates three challenges facing any semantic model for change information modeling from time series remote sensing data.
doi:10.3390/ijgi5100179 fatcat:k2oh2cizo5dttgtvlvjtrgkl6q