To address the issues of low extraction accuracy, inadequate data representation, and complex algorithm in urban landscape tree extraction, a new algorithm for extracting landscape trees from Light Detection and Ranging (LiDAR) data is proposed, which is based on the marked point process with spatial feature constraints. The proposed method removes nonground points from the point cloud data and constrains the marked point process model by the density of the crown point cloud of the trees. First, the triangulation network processing densification filtering algorithm is used to separate the ground and nonground points from the LiDAR point cloud data. Second, for nonground point data, the process model of spatial distribution identification points of landscape tree is defined and the geometry of landscape tree in ground projection area is described by a circle. The geometric model of tree crown projection area is defined, and the elevation distribution model is built using the elevation distribution characteristics of landscape tree and nontree area data points. Accordingly, an elevation constraint model is built based on the spatial density characteristics of the crown point cloud of the trees. The landscape tree extraction model is established using Bayesian theory integrating the above models, and the extraction model is simulated using the Reversible Jump Markov Chain Monte Carlo algorithm. Finally, the optimal landscape tree extraction results are obtained based on the maximum a posteriori probability criterion. The experimental results show that the proposed method’s overall accuracy of the landscape tree extraction is high and the overall extraction rate and accuracy are greater than 90%. It can also achieve high accuracy for the landscape tree extraction results of complex scenes with high recognition difficulty.