Ongoing urbanization processes have increased the demand for monitoring, controlling, and modeling services, with urban structure types as an initial interest. While urban land cover (LC) can be derived directly from high-resolution satellite images, urban land use (LU) is achieved through analyzing a combination of structural, functional, spatial, morphological, and topological attributes of the various LC classes. The objective of this letter is to distinguish urban LU classes on the basis of distances between buildings incorporated into a graph-based concept. The method was developed using cadastral data (ALK) for the German city of Rostock, then applied to the LC building objects derived from Quickbird data. Building distribution was examined and distances between buildings were used as an attribute for graph generation. Two graph measures (beta index, clustering coefficient) were analyzed resulting in two groups of LU categories. Transferability to a different urban area was tested without adaptions. Similar building distribution and LC extraction quality were found to be crucial for transferability tests. Distances between buildings are an important property for deriving LU classes, but should be accompanied by additional LC attributes to improve LU separability.