Urban tree species provide various essential ecosystem services in cities， such as regulating urban temperatures， reducing noise， capturing carbon， and mitigating the urban heat island effect. The quality of these services is influenced by species diversity， tree health， and the distribution and the composition of trees. Traditionally， data on urban trees has been collected through field surveys and manual interpretation of remote sensing images. In this study， we evaluated the effectiveness of multispectral airborne laser scanning （ALS） data in classifying 24 common urban roadside tree species in Espoo， Finland. Tree crown structure information， intensity features， and spectral data were used for classification. Eight different machine learning algorithms were tested， with the extra trees （ET） algorithm performing the best， achieving an overall accuracy of 71.7% using multispectral LiDAR data. This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy. Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.