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As forecast by the United Nations in May 2007, the population of the world transitioned from a rural to an urban
demographic majority with more than half living in urban areas.1 Modern urban environments are complex 3-
dimensional (3D) landscapes with 4-dimensional patterns of activity that challenge various traditional 1-dimensional and
2-dimensional sensors to accurately sample these man-made terrains. Depending on geographic location, data resulting
from LIDAR, multi-spectral, electro-optical, thermal, ground-based static and mobile sensors may be available with
multiple collects along with more traditional 2D GIS features. Reconciling differing data sources over time to correctly
portray the dynamic urban landscape raises significant fusion and representational challenges particularly as higher
levels of spatial resolution are available and expected by users. This paper presents a framework for integrating the
imperfect answers of our differing sensors and data sources into a powerful representation of the complex urban
environment. A case study is presented involving the integration of temporally diverse 2D, 2.5D and 3D spatial data
sources over Kandahar, Afghanistan. In this case study we present a methodology for validating and augmenting
2D/2.5D urban feature and attribute data with LIDAR to produce validated 3D objects. We demonstrate that nearly 15%
of buildings in Kandahar require understanding nearby vegetation before 3-D validation can be successful. We also
address urban temporal change detection at the object level. Finally we address issues involved with increased sampling
resolution since urban features are rarely simple cubes but in the case of Kandahar involve balconies, TV dishes, rooftop
walls, small rooms, and domes among other things.
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