Urban trees support our society in many ways, ranging from aesthetic landscapes to animal health and wellbeing (for human and non-human alike). Cataloguing urban trees to better understand these benefits is a challenge because they are largely contained on private land and therefore excluded from city inventories. LiDAR, a remote sensing technology, makes it possible to take images of vegetation from a distance without inconveniencing property-owners. LiDAR scanners can be mounted on vehicles for a relatively quick survey of a large area (like the city of Montreal). The goal of this project is to create a comprehensive inventory of Montreal’s urban trees and create a tool for other cities to do the same. We aim to develop a machine-learning algorithm that will accurately identify tree features and species based on LiDAR images derived from mobile laser scanning. Developing this efficient way of inventorying urban trees will allow city planners to better manage urban forests and the services they provide.