DocumentCode :
2990634
Title :
Building-based urban land use classification from vector databases in Manchester, UK
Author :
Hussain, Masroor ; Barr, Robet ; Chen, Dongmei
Author_Institution :
Dept. of Geogr., Queen´´s Univ., Kingston, ON, Canada
fYear :
2012
fDate :
15-17 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
The recognition, analysis and classification of urban structures are important in urban land use modeling. The form and the function of individual urban elements such as buildings and street blocks help us better understand the urban morphology. The types, layout and arrangement of these buildings form up the local characteristics of urban areas. A model has been developed to classify urban areas based on the cartometric properties of buildings and the patterns they make. Supervised and un-supervised classification algorithms from data mining techniques along with GIS are explored to help create a framework for extracting information from vector databases and classifying building and blocks. The methodology is developed and applied to Manchester metropolitan in the UK.
Keywords :
data mining; geographic information systems; information retrieval; land use planning; pattern classification; terrain mapping; visual databases; GIS; Manchester; UK; building blocks; building cartometric properties; building-based urban land use classification; information extraction framework; street blocks; supervised classification algorithm; unsupervised classification algorithm; urban area local characteristics; urban land use model; urban structure analysis; urban structure classification; urban structure recognition; vector databases; Buildings; Data Mining; Decision Tree; Geographic Information System (GIS); Structural Classification; Urban Areas;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoinformatics (GEOINFORMATICS), 2012 20th International Conference on
Conference_Location :
Hong Kong
ISSN :
2161-024X
Print_ISBN :
978-1-4673-1103-8
Type :
conf
DOI :
10.1109/Geoinformatics.2012.6270327
Filename :
6270327
Link To Document :
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