Min Yang

Bio: Min Yang is an academic researcher from Wuhan University. The author has contributed to research in topics: Cartographic generalization & Representation (mathematics). The author has an hindex of 8, co-authored 25 publications receiving 248 citations.

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.

Envelope generation and simplification of polylines using Delaunay triangulation

Abstract: As a basic and significant operator in map generalization, polyline simplification needs to work across scales. Perkal’s e-circle rolling approach, in which a circle with diameter e is rolled on both sides of the polyline so that the small bend features can be detected and removed, is considered as one of the few scale-driven solutions. However, the envelope computation, which is a key part of this method, has been difficult to implement. Here, we present a computational method that implements Perkal’s proposal. To simulate the effects of a rolling circle, Delaunay triangulation is used to detect bend features and further to construct the envelope structure around a polyline. Then, different connection methods within the enveloping area are provided to output the abstracted result, and a strategy to determine the best connection method is explored. Experiments with real land-use polygon data are implemented, and comparison with other algorithms is discussed. In addition to the scale-specificity inherited from Perkal’s proposal, the results show that the proposed algorithm can preserve the main shape of the polyline and meet the area-maintaining constraint during large-scale change. This algorithm is also free from self-intersection.

A vector field model to handle the displacement of multiple conflicts in building generalization

Abstract: In map generalization, the displacement operation attempts to resolve proximity conflicts to guarantee map legibility Owing to the limited representation space, conflicts may occur between both the same and different features under different contexts A successful displacement should settle multiple conflicts, suppress the generation of secondary conflicts after moving some objects, and preserve the distribution patterns The effect of displacement can be understood as a force that pushes related objects away with properties of propagation and distance decay This study borrows the idea of vector fields from physics discipline and establishes a vector field model to handle the displacement of multiple conflicts in building generalization A scalar field is first constructed based on a Delaunay triangulation skeleton to partition the buildings being examined eg, a street block Then, we build a vector field to conduct displacement measurements through the detection of conflicts from multiple sources The direction and magnitude of the displacement force are computed based on an iso-line model of vector field The experiment shows that this global method can settle multiple conflicts and preserve the spatial relations and important building patterns

Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps

Abstract: The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space a...

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.

Machine learning for landslides prevention: a survey

Abstract: Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention