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Gang Liu

Researcher at China University of Geosciences (Wuhan)

Publications -  41
Citations -  306

Gang Liu is an academic researcher from China University of Geosciences (Wuhan). The author has contributed to research in topics: Spatial database & Spatial analysis. The author has an hindex of 8, co-authored 41 publications receiving 208 citations.

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3D stochastic modeling framework for Quaternary sediments using multiple-point statistics: A case study in Minjiang Estuary area, southeast China

TL;DR: An integrated MPS-based 3D modeling framework is presented by incorporating the characteristics of Quaternary sediments and the datasets obtained from geological exploration to achieve more precise visualization for the subsurface structures.
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Changes of urban wetlands in Wuhan, China, from 1987 to 2005

TL;DR: Based on land-use maps, TM images, and field data from the Wuhan wetlands, the spatiotemporal evolution and wetland landscape pattern were quantitatively analyzed, with reference to landscape ecology indices of diversity, fragmentation, dominance, shape, and dimension as discussed by the authors.
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Conditional multiple-point geostatistical simulation for unevenly distributed sample data

TL;DR: A conditional MPS-based simulation method which considers the distribution characteristics of sample data adequately and avoids the excessive concentration of the nodes in a data event, which demonstrates their applicability in characterizing heterogeneous phenomena when faced with unevenly distributed conditioning data.
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Decomposition tree: a spatio-temporal indexing method for movement big data

TL;DR: A novel access method, the Decomposition Tree (D-tree), for indexing multi-dimensional movement data, which is a virtual tree without inner nodes through an encoding method based on integer bit-shifting operation, and can efficiently answer a wide range of queries.
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Local curvature entropy-based 3D terrain representation using a comprehensive Quadtree

TL;DR: An effective and comprehensive 3D terrain representation based on local curvature entropy and a dynamic Quadtree that is able to support the real-time dynamic scheduling of large scale terrain models more easily and efficiently is presented.