Urban trees play an important role in improving city liveability by reducing heat, air pollution, flood risk, and supporting a balanced, sustainable microclimate. Thus, detecting and monitoring urban trees are vital for an effective city management and environmental conservation. Traditional remote sensing methods rely on imagery from optical sensors but they face limitations in capturing inner tree structural information and LiDAR (Light Detection And Ranging) data can be a suitable alternative. Although point-cloud based approaches explore directly the three-dimensional information inherent in raw LiDAR data, the effectiveness of 3D descriptors and intensity values for tree detection can be assessed more thoroughly – specifically in the context of heterogenous and mixed trees compositions commonly found in urban environments. This work introduces an automatic and unsupervised approach for urban tree detection from airborne LiDAR data, combining intensity information with the omnivariance, a 3D descriptor based on eigenvalues. A two-step K-means clustering method is applied – first to identify potential tree points using intensity, then to detect actual trees using geometric feature – followed by morphological guided filtering to reduce misclassification. The testing was carried out on six different areas selected in datasets from Brazil and New Zealand. The evaluation was based on manually labelled reference data. The obtained results reveal an overall accuracy of 89% and low omission errors (6%), indicating method’s robustness across varied urban scenarios.