Green infrastructure (GI) is widely accepted as a cost-effective adaptation strategy to global warming and climate change by providing multiple regulating ecosystem services. However, a more comprehensive GI typology (GIT) is necessary to identify, classify and monitor baseline vegetation by covering larger urban areas within a short time. This paper responds to this gap and proposes a local-scaled classification method using airborne remote sensing data.  Two previous studies were conducted on how to categorise GI according to functional, structural and configurational attributes (Bartesaghi et al. 2016, 2015). As a result, a new standardised classification scheme and a GIT matrix were introduced. It is the aim of the present study to demonstrate their applicability through a representative pilot study in the City of Sydney. A new GIS-based workflow for the automated extraction and categorisation of GI from high resolution airborne LiDAR and hyperspectral imagery is presented. Due to the study scope and nature of data, we will only concentrate on mapping and classifying green open spaces and tree canopies. The resulting typology and workflow will enable the performance evaluation of urban greening across a variety of ecosystem services. The GIT can also be implemented by industry and local governments as a planning tool to benchmark and compare existing green cover conditions as well as to implement well-targeted planning, design and management interventions in the context of climate change adaptation and mitigation. We recommend further analysis to refine and test the derivative uses of this tool for other contexts and purposes.