Abstract
Abstract. Efficient management of roadside trees for local governments is important. Mobile Mapping System (MMS) equipped with a high-density LiDAR scanner has the possibility to be applied to estimate DBH of roadside trees using point clouds. In this study, we propose a method for detecting roadside trees and estimating their DBHs automatically from MMS point clouds. In our method, point clouds captured using the MMS are mapped on a 2D image plane, and they are converted into a wireframe model by connecting adjacent points. Then, geometric features are calculated for each point in the wireframe model. Tree points are detected using a machine learning technique. The DBH of each tree is calculated using vertically aligned circles extracted from the wireframe model. Our method allows robustly calculating the DBH even if there is a hump at breast height. We evaluated our method using actual MMS data measured in an urban area in Tokyo. Our method achieved a high extraction performance of 100 percent of precision and 95.1 percent of recall for 102 roadside trees. The average accuracy of the DBH was 2.0 cm. These results indicate that our method is useful for the efficient management of roadside trees.
Highlights
Roadside trees are planted by various local governments, because they have the effects such as forming street landscapes and suppressing the temperature rise in summer
Since there is often a discrepancy between the actual situation and the management ledger, the number and size of roadside trees are measured by field surveys in order to investigate the situation of roadside trees at a certain point in time
Diameter at Breast Height (DBH) is the diameter of the trunk at a certain height from the ground, but LiDAR does not guarantee to acquire a sufficient number of points at that height
Summary
Roadside trees are planted by various local governments, because they have the effects such as forming street landscapes and suppressing the temperature rise in summer. Since there is often a discrepancy between the actual situation and the management ledger, the number and size of roadside trees are measured by field surveys in order to investigate the situation of roadside trees at a certain point in time. The Mobile Mapping System (MMS) is useful for efficiently surveying roadside objects in a wide field. MMS equipped with a high-density LiDAR scanner is widely used for map creation and infrastructure management because it can acquire 3D information around the road as highdensity point clouds. We discuss methods suitable for detecting roadside trees and estimating the DBH from MMS. The double-blind peer-review was conducted on the basis of the full paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
