  As a vital indicator for measuring urban development, urban areas are
expected to be identified explicitly and conveniently with widely available
dataset thereby benefiting the planning decisions and relevant urban studies.
Existing approaches to identify urban areas normally based on mid-resolution
sensing dataset, socioeconomic information (e.g. population density) generally
associate with low-resolution in space, e.g. cells with several square
kilometers or even larger towns/wards. Yet, few of them pay attention to
defining urban areas with micro data in a fine-scaled manner with large extend
scale by incorporating the morphological and functional characteristics. This
paper investigates an automated framework to delineate urban areas in the
parcel level, using increasingly available ordnance surveys for generating all
parcels (or geo-units) and ubiquitous points of interest (POIs) for inferring
density of each parcel. A vector cellular automata model was adopted for
identifying urban parcels from all generated parcels, taking into account
density, neighborhood condition, and other spatial variables of each parcel. We
applied this approach for mapping urban areas of all 654 Chinese cities and
compared them with those interpreted from mid-resolution remote sensing images
and inferred by population density and road intersections. Our proposed
framework is proved to be more straight-forward, time-saving and fine-scaled,
compared with other existing ones, and reclaim the need for consistency,
efficiency and availability in defining urban areas with well-consideration of
omnipresent spatial and functional factors across cities.
