Jianzhong Guo
Biographic Data
| ID | 6311174 |
|---|---|
| NAME | Jianzhong Guo |
| GIVEN NAMES | Jianzhong |
| FAMILY NAME | Guo |
| SIGNATURE | GUO J |
| AFFILIATIONS | Henan University |
| ORCID | 0000-0003-1909-3510 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 4 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 1 |
Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network
Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road select…
Few-Shot Building Footprint Shape Classification with Relation Network
Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to la…
Using Attributes Explicitly Reflecting User Preference in a Self-Attention Network for Next POI Recommendation
With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning mode…
TriangleConv
The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a …
Exploring the rich-club characteristic in internal migration
TriangleConv
The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a …
Exploring the rich-club characteristic in internal migration
Few-Shot Building Footprint Shape Classification with Relation Network
Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to la…
Using Attributes Explicitly Reflecting User Preference in a Self-Attention Network for Next POI Recommendation
With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning mode…
Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network
Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research toward the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road select…
Computer Science (5 works) · Artificial Intelligence (4 works) · Geography (3 works) · Machine learning (3 works) · Automated Road and Building Extraction (2 works) · Data mining (2 works) · Graph (2 works) · Human Mobility and Location-Based Analysis (2 works) · Theoretical Computer Science (2 works) · Adjacency list (1 work)