A-Xing Zhu
University of Wisconsin-Madison
237 Papers
766 Citations
A-Xing Zhu is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Digital soil mapping & Soil map. The author has an hindex of 45, co-authored 227 publications. Previous affiliations of A-Xing Zhu include Ca' Foscari University of Venice & Chinese Academy of Sciences.
Chat about Author
Papers
A function-based linear map symbol building and rendering method using shader language
TL;DR: The efficiency of rendering linear map elements is substantially improved compared to using the graphics device interface plus (GDI+) and anti-grain geometry (AGG) methods; it also provides an applicable approach for developing map rendering systems.
23
Simple Digital Terrain Analysis Software(SimDTA 1.0) and Its Application in Fuzzy Classification of Slope Positions: Simple Digital Terrain Analysis Software(SimDTA 1.0) and Its Application in Fuzzy Classification of Slope Positions
23
Construction of Membership Functions for Soil Mapping using the Partial Dependence of Soil on Environmental Covariates Calculated by Random Forest
Canying Zeng,Lin Yang,A-Xing Zhu +2 more
TL;DR: In this article, the authors developed a method to construct membership functions representing knowledge of soil-environment relationships from partial dependence, which were used for mapping soil subgroups in Heshan, China, under the Soil Landscape Inference Model framework.
23
Best Management Practices Optimization at Watershed Scale: Incorporating Spatial Topology among Fields
TL;DR: In this article, a new method for BMPs optimization was proposed, which incorporated knowledge of BMP interactions into a multi-objective genetic algorithm (i.e., e-NSGAII) based on spatial topology among fields.
22
Knowledge discovery from area-class resource maps: Capturing prototype effects
TL;DR: The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area-class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes.
22