Yan-Wen Wang
Biographic Data
| ID | 2193740 |
|---|---|
| NAME | Yan-Wen Wang |
| GIVEN NAMES | Yan-Wen |
| FAMILY NAME | Wang |
| SIGNATURE | WANG Y |
| AFFILIATIONS | Institute of Geographic Sciences and Natural Resources Research |
| ORCID | 0000-0001-8070-2122 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Mapping Street Patterns with Network Science and Supervised Machine Learning
This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street …
Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants
Fine particulate matter (PM 2.5 ) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM 2.5 distributions in Chinese cities, thus helping form better policies for future PM 2.5 mitigation. Based…
Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis
Detection of craters is important not only for planetary research but also for engineering applications. Although the existing crater detection approaches (CDAs) based on terrain analysis consider the topographic information of craters, they do not take into account the spatial structural information of real craters. In this article, we propose an automatic crater detection approach by training random forest classifiers with data from legacy crat…
No prominent works on this page.
Spatio-Temporal Heterogeneity of the Relationships Between PM2.5 and Its Determinants
Fine particulate matter (PM 2.5 ) poses threat to human health in China, particularly in winter. The pandemic of coronavirus disease 2019 (COVID-19) led to a series of strict control measures in Chinese cities, resulting in a short-term significant improvement in air quality. This is a perfect case to explore driving factors affecting the PM 2.5 distributions in Chinese cities, thus helping form better policies for future PM 2.5 mitigation. Based…
Automatic Crater Detection by Training Random Forest Classifiers with Legacy Crater Map and Spatial Structural Information Derived from Digital Terrain Analysis
Detection of craters is important not only for planetary research but also for engineering applications. Although the existing crater detection approaches (CDAs) based on terrain analysis consider the topographic information of craters, they do not take into account the spatial structural information of real craters. In this article, we propose an automatic crater detection approach by training random forest classifiers with data from legacy crat…
Mapping Street Patterns with Network Science and Supervised Machine Learning
This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised machine learning to classify street networks into gridiron, organic, hybrid, and cul-de-sac patterns with the street-based local area (SLA) as the unit of analysis. Utilising quantitative street …
Artificial Intelligence (2 works) · Computer Science (2 works) · Geography (2 works) · Air pollution (1 work) · Air Quality and Health Impacts (1 work) · Air quality index (1 work) · Altimeter (1 work) · Artificial neural network (1 work) · Astrobiology (1 work) · Cartography (1 work)