Anzhu Yu
Biographic Data
| ID | 9966063 |
|---|---|
| NAME | Anzhu Yu |
| GIVEN NAMES | Anzhu |
| FAMILY NAME | Yu |
| SIGNATURE | YÜ A |
| AFFILIATIONS | PLA Information Engineering University |
| ORCID | 0000-0002-3332-9668 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Leveraging Deep Convolutional Neural Network for Point Symbol Recognition in Scanned Topographic Maps
Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address th…
Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data
Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we p…
No prominent works on this page.
Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data
Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we p…
Leveraging Deep Convolutional Neural Network for Point Symbol Recognition in Scanned Topographic Maps
Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address th…
Artificial Intelligence (2 works) · Computer Science (2 works) · 3D Surveying and Cultural Heritage (1 works) · Convolutional neural network (1 works) · Data mining (1 works) · Deep learning (1 works) · Graph (1 works) · Human Mobility and Location-Based Analysis (1 works) · Machine learning (1 works) · Mathematics (1 works)