Automated urban land-use classification with remote sensing

Abstract

Urban land-use information plays a key role in a wide variety of planning and environmental management processes. The purpose of this study was to develop an automatic method for classifying detailed urban land-use classes with remote-sensing data. Seven land-use parcel attributes, derived from relevant remote-sensing data, were incorporated for classifying four land-use classes, namely office, industrial, civic, and transportation, which were reported as the most difficult ones to classify from previous studies. An experiment was carried out in a study site in Austin, Texas. An overall accuracy of 61.68% and a kappa coefficient of 0.54 were achieved with a decision tree method. Building area and building height turned out to be the most influential factors among all the adopted variables. In addition, the variable of floor area ratio played the second dominant role among the seven variables, demonstrating that synthesized horizontal and vertical properties of buildings and their relevant spatial characteristics are important in differentiating the four classes.

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