Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

Date Published: 25 October 2010
PDF: 12 pages
Proc. SPIE 7831, Earth Resources and Environmental Remote Sensing/GIS Applications, 78310H (25 October 2010); doi: 10.1117/12.864930

Published in SPIE Proceedings Vol. 7831:
Earth Resources and Environmental Remote Sensing/GIS Applications
Ulrich Michel; Daniel L. Civco, Editor(s)