Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use.
Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in...
| Publicado en: | Annals of the Association of American Geographers Vol. 99; no. 1; pp. 76 - 99 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
January 2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511438628&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511438628 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00045608 AAG jtl: Annals of the Association of American Geographers issn: 00045608 maglogo: N pubinfo: dt: January 2009 vid: 99 iid: 1 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 511438628 10.1080/00045600802459028 ppf: 76 ppct: 23 formats: tig: atl: Using Geometrical, Textural, and Contextual Information of Land Parcels for Classification of Detailed Urban Land Use. aug: au: Wu, Shuo-Sheng Qiu, Xiaomin Usery, E. Lynn Wang, Le su: Classification Land use sug: subj: Classification Land use keyword: Austin (Tex.) -- Geography ab: Detailed urban land use data are important to government officials, researchers, and businesspeople for a variety of purposes. This article presents an approach to classifying detailed urban land use based on geometrical, textural, and contextual information of land parcels. An area of 6 by 14 km in Austin, Texas, with land parcel boundaries delineated by the Travis Central Appraisal District of Travis County, Texas, is tested for the approach. We derive fifty parcel attributes from relevant geographic information system (GIS) and remote sensing data and use them to discriminate among nine urban land uses: single family, multifamily, commercial, office, industrial, civic, open space, transportation, and undeveloped. Half of the 33,025 parcels in the study area are used as training data for land use classification and the other half are used as testing data for accuracy assessment. The best result with a decision tree classification algorithm has an overall accuracy of 96 percent and a kappa coefficient of 0.78, and two naive, baseline models based on the majority rule and the spatial autocorrelation rule have overall accuracy of 89 percent and 79 percent, respectively. The algorithm is relatively good at classifying single-family, multifamily, commercial, open space, and undeveloped land uses and relatively poor at classifying office, industrial, civic, and transportation land uses. The most important attributes for land use classification are the geometrical attributes, particularly those related to building areas. Next are the contextual attributes, particularly those relevant to the spatial relationship between buildings, then the textural attributes, particularly the semivariance texture statistic from 0.61-m resolution images. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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