An Over-Segmentation-Based Uphill Clustering Method for Individual Trees Extraction in Urban Street Areas From MLS Data

In this article, an over-segmentation-based uphill clustering method for individual extraction of urban street trees from mobile laser scanning data is proposed to solve the problem that the existing methods depend heavily on tree trunks and have poor extraction results in complex environments where the tree trunks are blocked by cars and green belts, and the crown touching or interlocking is large. First, supervoxels are generated by over-segmentation, so that the amount of original data is reduced and the boundaries of different objects are effectively preserved. Then, the potential tree crowns and trunks are obtained by extracting typical object structures. Finally, individual trees extraction is realized by extracting independent crowns from the potential crowns via uphill clustering and searching corresponding trunks from the potential trunks. The main contribution of this article is to propose an individual extraction method for street trees based on uphill clustering that does not rely on the extraction of tree trunks, which improves the completeness of extracted results in complex urban environments. The experimental results demonstrate that the proposed method effectively extracted the street trees individually from the test data, with the completeness of 100%, the correctness of 96.4%, and the F-score of 0.98. Moreover, the proposed method also achieves good result for the extraction of greening trees that are heavily blocked in the green belt areas. And the corresponding completeness, correctness, and the F-score are 94.6%, 83.3%, and 0.89, respectively.

[1]  Ning Ye,et al.  Automatic extraction of street trees' nonphotosynthetic components from MLS data , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Michela Bertolotto,et al.  Octree-based region growing for point cloud segmentation , 2015 .

[3]  Yuan Li,et al.  Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[4]  Quan Li,et al.  Comparison of Different Feature Sets for TLS Point Cloud Classification , 2018, Sensors.

[5]  Sheng Xu,et al.  Road Curb Extraction From Mobile LiDAR Point Clouds , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Sheng Xu,et al.  A New Clustering-Based Framework to the Stem Estimation and Growth Fitting of Street Trees From Mobile Laser Scanning Data , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Bo Yang,et al.  RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Ying Li,et al.  Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review , 2018, Remote. Sens..

[9]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Belén Riveiro,et al.  Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring , 2019, Infrastructures.

[11]  Bisheng Yang,et al.  USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS , 2016 .

[12]  Jianfeng Liu,et al.  Automated extraction of urban roadside trees from mobile laser scanning point clouds based on a voxel growing method , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[13]  Yiping Chen,et al.  Extraction of street trees from mobile laser scanning point clouds based on subdivided dimensional features , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[14]  Jian Yao,et al.  Segmentation-based classification for 3D urban point clouds , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[15]  Martin Simonovsky,et al.  Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[16]  Tony DeRose,et al.  Surface reconstruction from unorganized points , 1992, SIGGRAPH.

[17]  Enoc Sanz-Ablanedo,et al.  Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data , 2017, Sensors.

[18]  Bisheng Yang,et al.  Hierarchical extraction of urban objects from mobile laser scanning data , 2015 .

[19]  Cheng Wang,et al.  A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[20]  Michael Weinmann,et al.  A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas , 2017, Remote. Sens..

[21]  Bharat Lohani,et al.  Identification of trees and their trunks from mobile laser scanning data of roadway scenes , 2020, International Journal of Remote Sensing.

[22]  Lin Li,et al.  A dual growing method for the automatic extraction of individual trees from mobile laser scanning data , 2016 .

[23]  Hans-Gerd Maas,et al.  AUTOMATIC PROCESSING OF MOBILE LASER SCANNER POINT CLOUDS FOR BUILDING FAÇADE DETECTION , 2012 .

[24]  Xinchang Zhang,et al.  Geometric Primitives in LiDAR Point Clouds: A Review , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Ajai Kumar Singh,et al.  GENERATING GIS DATABASE OF STREET TREES USING MOBILE LIDAR DATA , 2018 .

[26]  Jianping Wu,et al.  A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data , 2013, Remote. Sens..

[27]  C. Fang,et al.  Investigation of the noise reduction provided by tree belts , 2003 .

[28]  W Fan,et al.  Automated extraction of urban trees from mobile LiDAR point clouds , 2016 .

[29]  Jonathan Li,et al.  Rapid Urban Roadside Tree Inventory Using a Mobile Laser Scanning System , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[30]  Wei Su,et al.  A method for extracting trees from vehicle-borne laser scanning data , 2013, Math. Comput. Model..

[31]  Ming Cheng,et al.  3-D Road Boundary Extraction From Mobile Laser Scanning Data via Supervoxels and Graph Cuts , 2018, IEEE Transactions on Intelligent Transportation Systems.

[32]  Juha Hyyppä,et al.  An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept , 2016, Remote. Sens..

[33]  Lin Li,et al.  A method based on an adaptive radius cylinder model for detecting pole-like objects in mobile laser scanning data , 2016 .

[34]  Jonathan Li,et al.  Generation of Horizontally Curved Driving Lines in HD Maps Using Mobile Laser Scanning Point Clouds , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  Ning Ye,et al.  A supervoxel approach to the segmentation of individual trees from LiDAR point clouds , 2018 .

[36]  Meng Wu,et al.  Extraction and Simplification of Building Façade Pieces from Mobile Laser Scanner Point Clouds for 3D Street View Services , 2016, ISPRS Int. J. Geo Inf..

[37]  Qi Zhang,et al.  Deep learning-based tree classification using mobile LiDAR data , 2015 .

[38]  Ajai Kumar Singh,et al.  POLE-SHAPED OBJECT DETECTION USING MOBILE LIDAR DATA IN RURAL ROAD ENVIRONMENTS , 2015 .

[39]  Jun Yu,et al.  Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Yanming Chen,et al.  Segmentation of Individual Trees From TLS and MLS Data , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  B. Koch,et al.  A Lidar Point Cloud Based Procedure for Vertical Canopy Structure Analysis And 3D Single Tree Modelling in Forest , 2008, Sensors.

[42]  Boris Jutzi,et al.  Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features , 2014 .

[43]  Roderik Lindenbergh,et al.  SigVox – A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds , 2017 .

[44]  Paul H. Lewis,et al.  The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data , 2017 .

[45]  Guowei Yue,et al.  A Method for Extracting Street Trees from Mobile LiDAR Point Clouds , 2015 .

[46]  Bisheng Yang,et al.  Extraction 3D road boundaries from mobile laser scanning point clouds , 2015, 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM).

[47]  George Vosselman,et al.  Pole-Like Road Furniture Detection and Decomposition in Mobile Laser Scanning Data Based on Spatial Relations , 2018, Remote. Sens..

[48]  Ajai Kumar Singh,et al.  Extraction of road surface from mobile LiDAR data of complex road environment , 2017 .

[49]  Cheng Wang,et al.  Toward better boundary preserved supervoxel segmentation for 3D point clouds , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[50]  Florentin Wörgötter,et al.  Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.