Tinghua Ai

Bio: 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.

Automated building generalization based on urban morphology and Gestalt theory

Abstract: Building generalization is a difficult operation due to the complexity of the spatial distribution of buildings and for reasons of spatial recognition. In this study, building generalization is decomposed into two steps, i.e. building grouping and generalization execution. The neighbourhood model in urban morphology provides global constraints for guiding the global partitioning of building sets on the whole map by means of roads and rivers, by which enclaves, blocks, superblocks or neighbourhoods are formed; whereas the local constraints from Gestalt principles provide criteria for the further grouping of enclaves, blocks, superblocks and/or neighbourhoods. In the grouping process, graph theory, Delaunay triangulation and the Voronoi diagram are employed as supporting techniques. After grouping, some useful information, such as the sum of the building's area, the mean separation and the standard deviation of the separation of buildings, is attached to each group. By means of the attached information, an ...

A graph convolutional neural network for classification of building patterns using spatial vector data

Abstract: Machine learning methods, specifically, convolutional neural networks (CNNs), have emerged as an integral part of scientific research in many disciplines. However, these powerful methods often fail to perform pattern analysis and knowledge mining with spatial vector data because in most cases, such data are not underlying grid-like or array structures but can only be modeled as graph structures. The present study introduces a novel graph convolution by converting it from the vertex domain into a point-wise product in the Fourier domain using the graph Fourier transform and convolution theorem. In addition, the graph convolutional neural network (GCNN) architecture is proposed to analyze graph-structured spatial vector data. The focus of this study is the classical task of building pattern classification, which remains limited by the use of design rules and manually extracted features for specific patterns. The spatial vector data representing grouped buildings are modeled as graphs, and indices for the characteristics of individual buildings are investigated to collect the input variables. The pattern features of these graphs are directly extracted by training labeled data. Experiments confirmed that the GCNN produces satisfactory results in terms of identifying regular and irregular patterns, and thus achieves a significant improvement over existing methods. In summary, the GCNN has considerable potential for the analysis of graph-structured spatial vector data as well as scope for further improvement.

Building pattern recognition in topographic data: examples on collinear and curvilinear alignments

Abstract: Building patterns are important features that should be preserved in the map generalization process. However, the patterns are not explicitly accessible to automated systems. This paper proposes a framework and several algorithms that automatically recognize building patterns from topographic data, with a focus on collinear and curvilinear alignments. For both patterns two algorithms are developed, which are able to recognize alignment-of-center and alignment-of-side patterns. The presented approach integrates aspects of computational geometry, graph-theoretic concepts and theories of visual perception. Although the individual algorithms for collinear and curvilinear patterns show great potential for each type of the patterns, the recognized patterns are neither complete nor of enough good quality. We therefore advocate the use of a multi-algorithm paradigm, where a mechanism is proposed to combine results from different algorithms to improve the recognition quality. The potential of our method is demonstrated by an application of the framework to several real topographic datasets. The quality of the recognition results are validated in an expert survey.

Computer and Robot Vision

Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Missing Information Reconstruction of Remote Sensing Data: A Technical Review

Abstract: Because of sensor malfunction and poor atmospheric conditions, there is usually a great deal of missing information in optical remote sensing data, which reduces the usage rate and hinders the follow-up interpretation. In the past decades, missing information reconstruction of remote sensing data has become an active research field, and a large number of algorithms have been developed. However, to the best of our knowledge, there has not, to date, been a study that has been aimed at expatiating and summarizing the current situation. This is therefore our motivation in this review. This paper provides an introduction to the principles and theories of missing information reconstruction of remote sensing data. We classify the established and emerging algorithms into four main categories, followed by a comprehensive comparison of them from both experimental and theoretical perspectives. This paper also predicts the promising future research directions.