Urban and landscape planners are becoming increasingly aware of the potential of light detection and ranging (lidar) technology to produce height and structural information over large geographic areas in both an economic and time-efficient fashion. In urban environments where the structural complexity is high, for example, lidar is seen as a critical and innovative dataset to improve the characterization of both vegetation and building attributes. Using a small-footprint, first- and last-return lidar dataset of Vancouver, Canada, we demonstrate the potential to derive a suite of attributes important for describing the interactions of the urban surface and atmosphere in weather forecasting, air pollution, and urban dispersion modelling. Two levels of attributes were defined. First, primary attributes such as building shape, size, and location and tree classification were calculated. Building extent and size were computed using an object-based approach based on connectivity and height rules. The classification of tree crown areas was derived from the location of last-return data, filtered to remove the incidence of last returns caused by the interaction of the lidar beam with building edges, and height rules. Validation showed that building areas derived from lidar compared well with aerial photography estimates (r2 = 0.96, p