Street tree segmentation from mobile laser scanning data

Mobile laser scanning (MLS) measurement technology is well-suited for individual-tree-based urban forest inventory because it can capture a dense three-dimensional (3D) point cloud from vertical structures of roadside trees in a rapid and cost-effective way. Street tree segmentation from MLS data is a preliminary and essential step for further extraction and analysis of tree attributes and is also a challenging problem because of the complex street environment. The accuracy of current methods is limited by information loss introduced by voxelization and artificially designed detection and segmentation rules based on prior knowledge that more or less lack ability to distinguish between street trees and other urban objects. To address the above issues, a two-step strategy for street tree segmentation method from MLS data was proposed, which worked on points directly instead of a set of voxels generated by spatial partition and made use of tree crown and trunk detectors trained by supervised learning algorithm. First, 16 local statistical features, including width, depth, elevation, dimensionality, and density features, were extracted from the sphere domain of each point using the grid index. Then these features were fused and tree crown and trunk detectors were trained from labelled MLS data of street through Discrete AdaBoost algorithm. Second, based on frame projection of point-wise prediction results of tree crown and trunk detectors, individual trees were located and segmented by exploiting non-connectivity between adjacent trees and adjacency within single trees. Experimental results have demonstrated that the proposed method can obtain point-wise street tree segmentation from MLS data with high accuracy tree crown and trunk detection without tedious detection or segmentation rules. The error rates of crown detector on test set were less than 0.3% accompanied by over 99.33% recall rates and over 98.59% precision rates and the error rates of trunk detector on test set were less than 0.06% accompanied by over 92.44% recall rates and over 94.14% precision rates.