Automated metric characterization of urban structure using building decomposition from very high resolution imagery

• Purely metric characterization of urban built-up structure.
• Automated procedure from morphological decomposition to final classification.
• Integrating multiple important built-up features into a single map view.
• Clear differentiation of structural areas by built-up metrics on the test site.

Classification approaches for urban areas are mostly of qualitative and semantic nature. They produce interpreted classes similar to those from land cover and land use classifications. As a complement to those classes, quantitative measures directly derived from the image could lead to a metric characterization of the urban area. While these metrics lack of qualitative interpretation they are able to provide objective measure of the urban structures.Such quantitative measures are especially important in rapidly growing cities since, beside of the growth in area, they can provide structural information for specific areas and detect changes. Rustenburg, which serves as test area for the present study, is amongst the fastest growing cities in South Africa. It reveals a heterogeneous face of housing and building structures reflecting social and/or economic differences often linked to the spatial distribution of industrial and local mining sites. Up to date coverage with aerial photographs is provided by aerial surveys in regular intervals. Also recent satellite systems provide imagery with suitable resolution. Using such set of very high resolution images a fully automated algorithm has been developed which outputs metric classes by systematically combining important measures of building structure. The measurements are gained by decomposition of buildings directly from the imagery and by using methods from mathematical morphology. The decomposed building objects serve as basis for the computation of grid statistics. Finally a systematic combination of the single features leads to combined metrical classes.For the dominant urban structures verification results indicate an overall accuracy of at least 80% on the single feature level and 70% for the combined classes.