Against the background of massive urban development, area-wide and up-to-date spatial information is in demand.
However, for many reasons this detailed information on the entire urban area is often not available or just not valid
anymore. In the event of a natural hazard – e.g. a river flood – it is a crucial piece of information for relief units to have
knowledge about the quantity and the distribution of the affected population. In this paper we demonstrate the abilities of
remotely sensed data towards vulnerability assessment or disaster management in case of such an event. By means of
very high resolution optical satellite imagery and surface information derived by airborne laser scanning, we generate a
precise, three-dimensional representation of the landcover and the urban morphology. An automatic, object-oriented
approach detects single buildings and derives morphological information – e.g. building size, height and shape – for a
further classification of each building into various building types. Subsequently, a top-down approach is applied to
distribute the total population of the city or the district on each individual building. In combination with information of
potentially affected areas, the methodology is applied on two German cities to estimate potentially affected population
with a high level of accurac
