ABSTRACT
In recent years, remote sensing has become an increasingly important data source to support urban planning and management due to the availability of very-high-resolution images. Some of these images have a spectral resolution of less than 0.5 m. Such images allow extraction of detailed information on various targets in urban areas with the help of object-based image analysis. In this chapter, several methods used for characterization of high-resolution remotely sensed images for urban areas are introduced. Section 2.2 discusses the general role of remote sensing data for the urban landscape. Section 2.3 outlines the application of high-resolution satellite imagery. Section 2.4 illustrates an early effort using QuickBird imagery to map urban land cover. Section 2.5 introduces the decision tree model approach for urban land cover classification. Section 2.6 explains a recent effort using object-based image analysis for urban land cover classification. Section 2.7 summarizes this chapter.
