.png){kind=icon}
返回
Optimization approaches for generalization and data abstraction
.png){kind=icon}
PRE
AI
DOI：10.1080/13658810500161179.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least-Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self-Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scenario describes how these methods can be combined to automatically yield a satisfying result for integrating two data sets of different scales.
Keyword:
optimization
generalization
data abstraction
spatial data
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.1
论文数:
2.7K
被引数:
9.3K
机构
暂无机构信息
引用论文
Immunocontraception of captive exotic species. I. Przewalski's horses (Equus przewalskii) and banteng (Bos javanicus).png)
Zoo Biology
Zoo BiologyIF0
PREAI
分享收藏
查看更多内容