Tinghua Ai
Biographic Data
| ID | 6313261 |
|---|---|
| NAME | Tinghua Ai |
| GIVEN NAMES | Tinghua |
| FAMILY NAME | Ai |
| SIGNATURE | AI T |
| AFFILIATIONS | Wuhan University |
| ORCID | 0000-0002-6581-9872 |
| VERIFIED | Yes |
| TOTAL WORKS | 17 |
| TOTAL CITATIONS | 20 |
| WORKS AS AUTHOR | 17 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2015 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 2 |
A Multi-Scale Hybrid Scene Geometric Similarity Measurement Method Using Heterogeneous Graph Neural Network
Geographic features in maps consist of a mixture of points, polylines, and polygons, generally including POIs, roads, buildings, and other geographic features. Due to the differing dimensionality of these various types of geographic data, traditional geometric similarity measurement methods that rely on a single type of feature are not applicable to mixed scenes. The traditional solution to this issue is to treat points as projections of polyline…
A Selection Method of Massive Point Cluster Using the Delaunay Triangulation to Support Real-Time Visualization
One of the goals of map generalization is to achieve real-time visualization of massive entities while adapting to zoom-in/zoom-out conditions. Unlike traditional map generalization, this type of scaling operation does not simplify the data to produce a final result; it only outputs temporary visualization data. To meet the current visualization scale requirements, we insert a simplification algorithm prior to visualization to process the data. T…
A graph-based neural network approach to integrate multi-source data for urban building function classification
Machine learning in cartography
International audience
Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation
The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can…
A point selection method in map generalization using graph convolutional network model
For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manua…
Characterizing the activity patterns of outdoor jogging using massive multi-aspect trajectory data
Impact of the Covid-19 pandemic on urban human mobility - A multiscale geospatial network analysis using New York bike-sharing data
A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data
Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can o…
A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization
As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodo…
A Method for Generating Variable-Scale Maps for Small Displays
With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale method that can allow users to properly specify the size, shape, and number of the focus area(s). Our met…
Recognition of building group patterns using graph convolutional network
Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network base…
A parallel annotation placement method for dense point of interest labels using hexagonal grid
Point feature label placement (PFLP) has been a fundamental problem in automatic cartography over decades. In this research, labels are approximated by individual characters to offer more freedom of placement on raster-based grids, including allowing labels turning to avoid occlusions. Considering that parallel orientation of label annotation increases the label chances to resolve the dense conflicts, and that hexagonal grids provide more orienta…
A graph convolutional neural network for classification of building patterns using spatial vector data
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 convolut…
Origin–destination (OD) of the interprovincial floating population of China
China’s large floating population catalyzes economic development, redistributes the population, and promotes social changes to the social structure, lifestyle patterns, and cultural values. The floating population is a unique group faced with numerous problems stemming from cultural and lifestyle differences. Understanding and visualizing the distributional characteristics and patterns of the floating population is crucial for developing effectiv…
Fourier-based multi-scale representation and progressive transmission of cartographic curves on the internet
Automatic generation of multi-scale representations from the same spatial data source has been the research focus in map generalization for a long time. Based on the Fourier technique, this paper proposes a continuous, multi-scale representation model for progressive transformation of cartographic curves on the Internet. In our method, all the curves, whether closed or open, are depicted as periodical functions which are further expressed as Four…
The analysis and delimitation of Central Business District using network kernel density estimation
Impact of the Covid-19 pandemic on urban human mobility - A multiscale geospatial network analysis using New York bike-sharing data
The analysis and delimitation of Central Business District using network kernel density estimation
Recognition of building group patterns using graph convolutional network
Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network base…
Machine learning in cartography
International audience
A point selection method in map generalization using graph convolutional network model
For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manua…
Fourier-based multi-scale representation and progressive transmission of cartographic curves on the internet
Automatic generation of multi-scale representations from the same spatial data source has been the research focus in map generalization for a long time. Based on the Fourier technique, this paper proposes a continuous, multi-scale representation model for progressive transformation of cartographic curves on the Internet. In our method, all the curves, whether closed or open, are depicted as periodical functions which are further expressed as Four…
Fourier-based multi-scale representation and progressive transmission of cartographic curves on the internet
Automatic generation of multi-scale representations from the same spatial data source has been the research focus in map generalization for a long time. Based on the Fourier technique, this paper proposes a continuous, multi-scale representation model for progressive transformation of cartographic curves on the Internet. In our method, all the curves, whether closed or open, are depicted as periodical functions which are further expressed as Four…
The analysis and delimitation of Central Business District using network kernel density estimation
Origin–destination (OD) of the interprovincial floating population of China
China’s large floating population catalyzes economic development, redistributes the population, and promotes social changes to the social structure, lifestyle patterns, and cultural values. The floating population is a unique group faced with numerous problems stemming from cultural and lifestyle differences. Understanding and visualizing the distributional characteristics and patterns of the floating population is crucial for developing effectiv…
A graph convolutional neural network for classification of building patterns using spatial vector data
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 convolut…
A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization
As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodo…
A Method for Generating Variable-Scale Maps for Small Displays
With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale method that can allow users to properly specify the size, shape, and number of the focus area(s). Our met…
Recognition of building group patterns using graph convolutional network
Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network base…
A parallel annotation placement method for dense point of interest labels using hexagonal grid
Point feature label placement (PFLP) has been a fundamental problem in automatic cartography over decades. In this research, labels are approximated by individual characters to offer more freedom of placement on raster-based grids, including allowing labels turning to avoid occlusions. Considering that parallel orientation of label annotation increases the label chances to resolve the dense conflicts, and that hexagonal grids provide more orienta…
A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data
Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can o…
Characterizing the activity patterns of outdoor jogging using massive multi-aspect trajectory data
Impact of the Covid-19 pandemic on urban human mobility - A multiscale geospatial network analysis using New York bike-sharing data
Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation
The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can…
A point selection method in map generalization using graph convolutional network model
For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manua…
A graph-based neural network approach to integrate multi-source data for urban building function classification
Machine learning in cartography
International audience
A Multi-Scale Hybrid Scene Geometric Similarity Measurement Method Using Heterogeneous Graph Neural Network
Geographic features in maps consist of a mixture of points, polylines, and polygons, generally including POIs, roads, buildings, and other geographic features. Due to the differing dimensionality of these various types of geographic data, traditional geometric similarity measurement methods that rely on a single type of feature are not applicable to mixed scenes. The traditional solution to this issue is to treat points as projections of polyline…
A Selection Method of Massive Point Cluster Using the Delaunay Triangulation to Support Real-Time Visualization
One of the goals of map generalization is to achieve real-time visualization of massive entities while adapting to zoom-in/zoom-out conditions. Unlike traditional map generalization, this type of scaling operation does not simplify the data to produce a final result; it only outputs temporary visualization data. To meet the current visualization scale requirements, we insert a simplification algorithm prior to visualization to process the data. T…
Computer Science (16 works) · Artificial Intelligence (13 works) · Geography (11 works) · Mathematics (11 works) · Data mining (10 works) · Geographic Information Systems Studies (10 works) · Cartography (8 works) · Data Management and Algorithms (7 works) · Human Mobility and Location-Based Analysis (7 works) · Graph (5 works)