Urban tree species provide various essential ecosystem services in cities， such as mediating urban temperature， isolating noise， fixing carbon， and mitigating the urban heat island impact. The quality of these services is influenced by species diversity， tree growth status， and the distribution and composition of trees. Traditionally， data about urban trees has been gathered through field data collection and manual interpretation of remote sensing images. In this study， we evaluate the capacity of using Multispectral Airborne Laser Scanning （ALS） data to classify 24 common urban roadside tree species in Espoo， Finland. We utilized tree crown structure information， intensity features， and spectral information for classification. 8 different machine learning algorithms were used in our study and Extra trees （ET） performed best with an overall accuracy of 71.7% using multispectral LiDAR data， highlighting that combining structural and spectral information in a single frame could enhance classification accuracy. In the future， we will focus on identifying the most important features in species classification and finding algorithms with higher efficiency and accuracy.