邓吉秋
  • 学位:博士学位
  • 职称:副教授
  • 学科:地质资源与地质工程. 测绘科学与技术
  • 所在单位:地球科学与信息物理学院

副教授 博士生导师 硕士生导师

入职时间:1999-06-01
所在单位:地球科学与信息物理学院
学历:博士研究生毕业
办公地点:校本部地学楼
联系方式:13874950729;QQ:188662140
学位:博士学位
在职信息:在职
毕业院校:中南大学

学科:地质资源与地质工程
测绘科学与技术

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Multiple Geographical Feature Label Placement Based on Multiple Candidate Positions in Two Degrees of Freedom Space
点击次数:
DOI码:
10.1109/ACCESS.2021.3120289
发表刊物:
IEEE Access
关键字:
Feature label placement,NP-hard problem,discrete differential evolution and genetic algorithm,multiple candidate positions,two degrees of freedom space
摘要:
Automatic multiple geographical feature label placement (MGFLP) is a combinatorial optimization problem shown to be an NP-hard problem, and it is a challenge in automatic cartography. Many automatic label placement algorithms for point, line, and area features were put forward. It is a common way to use multiple candidate positions (MCP) for label placement, but the research in this way mostly focuses on point features and does not take all three types of features and all the possible candidate positions into account on the map. Therefore, in this paper, the concept of degrees of spatial freedom for feature label placement is proposed based on the idea of degrees of freedom of mechanical motion. We define the degrees of freedom (DOF) and its space for feature labels on a planar map so as the potential space, including all the optional candidate positions of each feature label, can be standardized. Based on two degrees of freedom (2-DOF) space, feature reference position (FRP), and certain buffer distance (CBD) from FRP, we studied the methods including generating, calculating, evaluating, and selecting MCP for feature label. By using and improving the discrete differential evolution genetic algorithm (DDEGA), we carried out MGFLP experiments on the same dataset used by DDEGA algorithm. The results show that: 1) although the MCP based on the 2-DOF space increase the complexity of the NP-hard problem, however, the obtained results by optimizing the performance of the algorithm and increasing the number of candidate positions are still better than the traditional 8-candidate positions model. 2) In the same 2-DOF space, increasing the candidate positions from less to more along each direction of the 2-DOF space improves the quality of label placement.
合写作者:
Zhiyong Guo, Mohammad Naser Lessani*
第一作者:
Jiqiu Deng
论文类型:
期刊论文
文献类型:
J
卷号:
9
页面范围:
144085-144105
是否译文:
收录刊物:
SCI
个人简介

邓吉秋,男,湖南益阳人,博士,副教授,地理信息系副主任。

从事地学大数据与人工智能、网络GIS与移动GIS、地学三维建模与可视化等研究与教学,及相关信息系统和工程的设计与开发。


近期聚焦方向:

MultiGPT(多用途生成式先验转换模型)及其在智慧城市、自然资源、地质灾害、医疗健康、文化旅游等领域的应用。


现主持科研项目:

[1]. 矿山开发治理方案智能提取与管理工具(横向,2024年10月-)

[2]. 地质资料关键信息提取工具横向,2024年10月-)

[3]. 矿业权全生命周期信息化管理方案(横向,2023年11月-)

[4]. 重金属来源-暴露途径-重大疾病关系链(省重点研发课题,2023年7月-)

[5]. 车辆位置刷新智能算法和GIS服务(横向,2022年12月-)

[6]. 湖南沅陵官庄金矿床三维地质建模及成矿预测(横向,2022年11月-)

[7]. 面向三维成矿预测的多源异构地质资料钻孔数据智能抽取与结构化方法(国自科面上项目,2022年1月-)

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