Mingwu Guo
Biographic Data
| ID | 9966424 |
|---|---|
| NAME | Mingwu Guo |
| GIVEN NAMES | Mingwu |
| FAMILY NAME | Guo |
| SIGNATURE | GUO M |
| AFFILIATIONS | Wuhan Geomatics Institute, Wuhan 430022, China |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
A Convolutional Autoencoder-Based Method for Vector Curve Data Compression
(1) Background: Curve data compression plays a critical role in efficient storage, transmission, and multi-scale visualization of vector spatial data, especially for complex geographic boundaries. Achieving high compression efficiency while preserving geometric fidelity remains a challenging task. (2) Methods: This study proposes a vector curve compression framework based on a convolutional autoencoder. Curve data are segmented and resampled to u…
Drainage Pattern Recognition of River Network Based on Graph Convolutional Neural Network
Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern …
No prominent works on this page.
Drainage Pattern Recognition of River Network Based on Graph Convolutional Neural Network
Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern …
A Convolutional Autoencoder-Based Method for Vector Curve Data Compression
(1) Background: Curve data compression plays a critical role in efficient storage, transmission, and multi-scale visualization of vector spatial data, especially for complex geographic boundaries. Achieving high compression efficiency while preserving geometric fidelity remains a challenging task. (2) Methods: This study proposes a vector curve compression framework based on a convolutional autoencoder. Curve data are segmented and resampled to u…
Advanced Data Compression Techniques (1 work) · Artificial Intelligence (1 work) · Automated Road and Building Extraction (1 work) · Compression ratio (1 work) · Computer Graphics and Visualization Techniques (1 work) · Computer Science (1 work) · Convolutional neural network (1 work) · Data compression (1 work) · Data Management and Algorithms (1 work) · Data mining (1 work)