Urban trees provide a range of vital social and environmental services. Currently, inventories of individual urban trees are conducted in-situ by professional arborists. Such an approach to urban tree inventories means they are challenging to maintain and only capture information describing trees on accessible land. Whilst remote sensing approaches have shown the potential to derive individual tree attributes, these studies rarely make use of existing inventory information. In this study, we present a method to parameterise an algorithm for individual tree detection and delineation from airborne remote sensing data. The approach uses existing inventory data as training information firstly for the detection of the canopy area and secondly to parameterise a marker-based watershed segmentation algorithm. In this parameterisation, crown segmentation shape, as well as features derived from the remote sensing data, are used to determine if a segment contains one or more trees. If a segment contains more than one tree, it is split with the number of markers increased until each segment includes only one tree. The approach was evaluated within three distinct urban areas: the central business district, an urban park and a residential area, to be determined. Commission and omission errors ranged between 11% and 27% across the three regions, with commission typically caused by land covers on private land that were unaccounted for in the training processes. In all areas the overall tree count was within two per cent of that defined by reference information. The accurate tree count produced by this approach suggests it has the potential to be adopted by government agencies for routine tree inventory maintenance.