Tianyuan Xiao
Biographic Data
| ID | 6746489 |
|---|---|
| NAME | Tianyuan Xiao |
| GIVEN NAMES | Tianyuan |
| FAMILY NAME | Xiao |
| SIGNATURE | XIAO T |
| AFFILIATIONS | Wuhan University |
| ORCID | 0000-0001-6337-4649 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
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 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 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 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 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…
Artificial Intelligence (3 works) · Computer Science (3 works) · 3D Modeling in Geospatial Applications (2 works) · Data Management and Algorithms (2 works) · Geographic Information Systems Studies (2 works) · Graph (2 works) · Mathematics (2 works) · Theoretical Computer Science (2 works) · Algorithm (1 work) · Artificial neural network (1 work)