T
Tinghua Ai
Researcher at Wuhan University
Publications - 141
Citations - 2298
Tinghua Ai is an academic researcher from Wuhan University. The author has contributed to research in topics: Cartographic generalization & Generalization. The author has an hindex of 22, co-authored 130 publications receiving 1708 citations. Previous affiliations of Tinghua Ai include Hong Kong Polytechnic University & Delft University of Technology.
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Automated building generalization based on urban morphology and Gestalt theory
TL;DR: The methodology described brings together a number of well-developed theories/techniques, including graph theory, Delaunay triangulation, the Voronoi diagram, urban morphology and Gestalt theory, in such a way that multiscale products can be derived.
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A graph convolutional neural network for classification of building patterns using spatial vector data
TL;DR: A novel graph convolution is introduced by converting it from the vertex domain into a point-wise product in the Fourier domain using the graph Fourier transform and convolution theorem, which achieves a significant improvement over existing methods.
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The analysis and delimitation of Central Business District using network kernel density estimation
TL;DR: A concentration index is presented to visualize the functional urban environment by means of a density surface, which is refined with network distances rather than Euclidean ones, and an efficient way supported by flow extension simulation is proposed.
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A land-use spatial optimization model based on genetic optimization and game theory
Yaolin Liu,Yaolin Liu,Wei Tang,Jianhua He,Jianhua He,Yanfang Liu,Yanfang Liu,Tinghua Ai,Tinghua Ai,Dianfeng Liu,Dianfeng Liu +10 more
TL;DR: A loosely coupled model based on a genetic algorithm and game theory is constructed to improve the ability of existing land-use spatial optimization models for addressing local land- use competitions (the competitions on land units).
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Building pattern recognition in topographic data: examples on collinear and curvilinear alignments
TL;DR: A framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments are proposed, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality.