Using remote sensing data and cluster algorithms to structure cities

被引：2

作者：

Tiessen, Thomas ^{[1
]}

Friesen, John ^{[1
]}

Rausch, Lea ^{[1
]}

Pelz, Peter E. ^{[1
]}

机构：

[1] Tech Univ Darmstadt, Chair Fluid Syst, Darmstadt, Germany

来源：

2019 JOINT URBAN REMOTE SENSING EVENT (JURSE) | 2019年

关键词：

clustering; k-medoid; CHAMELEON; DBSCAN; slums;

D O I：

10.1109/jurse.2019.8808973

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The increasing urban population and the resulting lack of reliable water, energy and food supply is a big challenge for cities especially in informal settlements (slums). In order to plan new, better supply structures for cities, it is useful to subdivide the cities into sub structures. This subdivision can be performed by using cluster algorithms. In this paper we subdivide the slums in Dhaka using three different cluster methods and evaluate them with different indicators regarding their suitability for infrastructure planning.

引用

页数：4

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