Cheng-Zhi Qin
Biographic Data
| ID | 2193741 |
|---|---|
| NAME | Cheng-Zhi Qin |
| GIVEN NAMES | Cheng-Zhi |
| FAMILY NAME | Qin |
| SIGNATURE | QIN C |
| AFFILIATIONS | Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application |
| ORCID | 0000-0002-5910-9807 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 1 |
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…
Formalizing Parameter Constraints to Support Intelligent Geoprocessing
Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel …
Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data
Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil–environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locat…
Knowledge Discovery from Area–Class Resource Maps
This paper presents a knowledge discovery approach to extracting knowledge from area–class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non–uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) al…
Knowledge Discovery from Area–Class Resource Maps
This paper presents a knowledge discovery approach to extracting knowledge from area–class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non–uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) al…
Knowledge Discovery from Area–Class Resource Maps
This paper presents a knowledge discovery approach to extracting knowledge from area–class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non–uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) al…
Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data
Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soil–environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the location-skipping scheme does not predict these locat…
Formalizing Parameter Constraints to Support Intelligent Geoprocessing
Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel …
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…
Computer Science (4 works) · Artificial Intelligence (2 works) · Geographic Information Systems Studies (2 works) · Mathematics (2 works) · Soil Geostatistics and Mapping (2 works) · Altimeter (1 work) · Astrobiology (1 work) · Cartography (1 work) · Class (philosophy (1 work) · Covariate (1 work)