Haizhong Qian
Biographic Data
| ID | 6745971 |
|---|---|
| NAME | Haizhong Qian |
| GIVEN NAMES | Haizhong |
| FAMILY NAME | Qian |
| SIGNATURE | QIAN H |
| AFFILIATIONS | PLA Information Engineering University |
| ORCID | 0000-0002-1255-7872 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 13 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Contour matching for multisource terrain data based on morphological classification and unsupervised learning
Continuous multi-scale transformation of building footprints
Underpinned by computer technology and intelligent devices, the display of geospatial elements has outstripped the constraints of traditional serial scale frameworks, progressing toward continuous multi-scale representation. Buildings are one of the most fundamental geospatial elements. Addressing the issues of feature point mismatching and unreasonable deformation of concave and convex parts of a building contour found in other continuous repres…
End-to-End Vector Simplification for Building Contours via a Sequence Generation Model
Simplifying building contours involves reducing data volume while preserving the continuity, accuracy, and essential characteristics of building shapes. This presents significant challenges for sequence representation and generation. Traditional methods often rely on complex rule design, feature engineering, and iterative optimization. To overcome these limitations, this study proposes a Transformer-based Polygon Simplification Model (TPSM) for t…
Cognitive hierarchical matching approach for multi-scale buildings based on dynamic simulated generalization algorithm and iterative position adjustment
An intelligent multilane roadway recognition method based on pseudo-tagging
Recognition and Classification of Typical Building Shapes Based on Yolo Object Detection Models
The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recogni…
Progressive merging of blocks taking into account functional semantics and grid features
Continuous-scale representation of urban blocks is critical for enriching the web-based mapping experience in the modern era. Traditionally, continuous-scale merging methods for surface elements have been employed to deal with landscape features, such as patches and land-cover data. In comparison, the artificially constructed urban blocks offer complex semantics and unique shapes. Traditional methods face challenges in maintaining their fundament…
CatBoost-Based Automatic Classification Study of River Network
Existing research on automatic river network classification methods has difficulty scientifically quantifying and determining feature threshold settings and evaluating weights when calculating multi-indicator features of the local and overall structures of river reaches. In order to further improve the accuracy of river network classification and evaluate the feature weight, this paper proposes an automatic grading method for river networks based…
Simplification and Regularization Algorithm for Right-Angled Polygon Building Outlines with Jagged Edges
Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular boundaries, redundant points, inaccurate positions, and unclear turns arising from variations in the image quality, the complexity of the surrounding environment, and the extraction methods used, imped…
A Method for Intelligent Road Network Selection Based on Graph Neural Network
As an essential role in cartographic generalization, road network selection produces basic geographic information across map scales. However, the previous selection methods could not simultaneously consider both attribute characteristics and spatial structure. In light of this, an intelligent road network selection method based on a graph neural network (GNN) is proposed in this paper. Firstly, the selection case is designed to construct a sample…
A Method for Regularizing Buildings through Combining Skeleton Lines and Minkowski Addition
With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this author for extracting the skeleton lines of buildings through integrating vector and raster data using jagged building skeleton lines as the input data, a new method is proposed here for regularizing…
A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest
As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI l…
Extracting Skeleton Lines from Building Footprints by Integration of Vector and Raster Data
The extraction of skeleton lines of buildings is a key step in building spatial analysis, which is widely performed for building matching and updating. Several methods for vector data skeleton line extraction have been established, including the improved constrained Delaunay triangulation (CDT) and raster data skeleton line extraction methods, which are based on image processing technologies. However, none of the existing studies have attempted t…
A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest
As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI l…
Extracting Skeleton Lines from Building Footprints by Integration of Vector and Raster Data
The extraction of skeleton lines of buildings is a key step in building spatial analysis, which is widely performed for building matching and updating. Several methods for vector data skeleton line extraction have been established, including the improved constrained Delaunay triangulation (CDT) and raster data skeleton line extraction methods, which are based on image processing technologies. However, none of the existing studies have attempted t…
CatBoost-Based Automatic Classification Study of River Network
Existing research on automatic river network classification methods has difficulty scientifically quantifying and determining feature threshold settings and evaluating weights when calculating multi-indicator features of the local and overall structures of river reaches. In order to further improve the accuracy of river network classification and evaluate the feature weight, this paper proposes an automatic grading method for river networks based…
Simplification and Regularization Algorithm for Right-Angled Polygon Building Outlines with Jagged Edges
Building outlines are important for emergency response, urban planning, and change analysis and can be quickly extracted from remote sensing images and raster maps using deep learning technology. However, such building outlines often have irregular boundaries, redundant points, inaccurate positions, and unclear turns arising from variations in the image quality, the complexity of the surrounding environment, and the extraction methods used, imped…
A Method for Intelligent Road Network Selection Based on Graph Neural Network
As an essential role in cartographic generalization, road network selection produces basic geographic information across map scales. However, the previous selection methods could not simultaneously consider both attribute characteristics and spatial structure. In light of this, an intelligent road network selection method based on a graph neural network (GNN) is proposed in this paper. Firstly, the selection case is designed to construct a sample…
A Method for Regularizing Buildings through Combining Skeleton Lines and Minkowski Addition
With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this author for extracting the skeleton lines of buildings through integrating vector and raster data using jagged building skeleton lines as the input data, a new method is proposed here for regularizing…
Recognition and Classification of Typical Building Shapes Based on Yolo Object Detection Models
The recognition and classification of building shapes are the prerequisites and foundation for building simplification, matching, and change detection, which have always been important research problems in the field of cartographic generalization. Due to the ambiguity and uncertainty of building shape outlines, it is difficult to describe them using unified rules, which has always limited the quality and automation level of building shape recogni…
Progressive merging of blocks taking into account functional semantics and grid features
Continuous-scale representation of urban blocks is critical for enriching the web-based mapping experience in the modern era. Traditionally, continuous-scale merging methods for surface elements have been employed to deal with landscape features, such as patches and land-cover data. In comparison, the artificially constructed urban blocks offer complex semantics and unique shapes. Traditional methods face challenges in maintaining their fundament…
End-to-End Vector Simplification for Building Contours via a Sequence Generation Model
Simplifying building contours involves reducing data volume while preserving the continuity, accuracy, and essential characteristics of building shapes. This presents significant challenges for sequence representation and generation. Traditional methods often rely on complex rule design, feature engineering, and iterative optimization. To overcome these limitations, this study proposes a Transformer-based Polygon Simplification Model (TPSM) for t…
Cognitive hierarchical matching approach for multi-scale buildings based on dynamic simulated generalization algorithm and iterative position adjustment
An intelligent multilane roadway recognition method based on pseudo-tagging
Contour matching for multisource terrain data based on morphological classification and unsupervised learning
Continuous multi-scale transformation of building footprints
Underpinned by computer technology and intelligent devices, the display of geospatial elements has outstripped the constraints of traditional serial scale frameworks, progressing toward continuous multi-scale representation. Buildings are one of the most fundamental geospatial elements. Addressing the issues of feature point mismatching and unreasonable deformation of concave and convex parts of a building contour found in other continuous repres…
Computer Science (11 works) · Artificial Intelligence (10 works) · Automated Road and Building Extraction (5 works) · Mathematics (5 works) · Algorithm (4 works) · Data mining (4 works) · Computer vision (3 works) · Data Management and Algorithms (3 works) · Geography (3 works) · Geometry (3 works)