Terrestrial laser scanning technology provides an efficient and accuracy solution for acquiring three-dimensional information of plants. The leaf-wood classification of plant point cloud data is a fundamental step for some forestry and biological research. An automatic sampling and training method for classification was proposed based on tree point cloud data. The plane fitting method was used for selecting leaf sample points and wood sample points automatically, then two local features were calculated for training and classification by using support vector machine (SVM) algorithm. The point cloud data of ten trees were tested by using the proposed method and a manual selection method. The average correct classification rate and kappa coefficient are 0.9305 and 0.7904, respectively. The results show that the proposed method had better efficiency and accuracy comparing to the manual selection method.
Beijing Forestry University（北京林业大学）；Research Institute of Petroleum Exploration and Development, Petrochina（中国石油勘探开发研究院）
Terrestrial laser scanning (TLS) can obtain tree point cloud with high precision and high density. Efficient classification of wood points and leaf points is essential to study tree structural parameters and ecological characteristics. By using both the intensity and spatial information, a three-step classification and verification method was proposed to achieve automated wood-leaf classification. Tree point cloud was classified into wood points and leaf points by using intensity threshold, neighborhood density and voxelization successively. Experiment was carried in Haidian Park, Beijing, and…
Tree skeleton plays an important role in tree structure analysis, forest inventory and ecosystem monitoring. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed. In this method, a wood-leaf classification algorithm was introduced to filter leaf points for the reduction of the leaf interference on tree skeleton generation, tree voxel thinning was adopted to extract raw tree skeleton quickly, and a breakpoint connection algorithm was used to…
There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of agriculture and forestry. Interpretation of LiDAR point clouds is increasingly relying on data-driven methods (such as supervised machine learning) that rely on large quantities of hand-labelled data. As this data is potentially expensive to capture, and difficult to clearly visualise and label manually, a means of supplementing real LiDAR scans with simulated data is becoming a necessary step in realising the potential of these methods. We present an open so…
Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea（景观建筑与乡村系统工程系，首尔国立大学，首尔，韩国）；Interdisciplinary Program in Landscape Architecture, Seoul National University, South Korea（景观建筑跨学科项目，首尔国立大学，韩国）；Integrated Major in Smart City Global Convergence, Seoul National University, Seoul, Republic of Korea（智能城市全球融合整合专业，首尔国立大学，首尔，韩国）；Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea（农业与生命科学研究院，首尔国立大学，首尔，韩国）
Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理研究 institute FGI，芬兰国家土地测绘局）；D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（3D光学计量（3DOM）单元，布鲁诺·凯瑟尔基金会（FBK））；Department of Built Environment, Aalto University, School of Engineering（环境学院，艾尔托大学，工程学院）
MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel
Department of Mechanical Engineering, Iowa State University, Ames, USA（机械工程系，爱荷华州立大学）；Translational AI Research and Education Center, Ames, USA（转化人工智能研究与教育中心）；Plant Science Institute, Iowa State University, Ames, USA（植物科学研究所）；Interdepartmental Genetics and Genomics Graduate Program, Iowa State University, Ames, USA（跨部门遗传学和基因组学研究生项目）；Department of Agronomy, Iowa State University, Ames, USA（农业学系）
treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds
University of Potsdam（波恩大学）；Digital Engineering Faculty（数字工程学院）；BOKU University（博科尼大学）；Department of Ecosystem Management, Climate and Biodiversity（生态系统管理、气候与生物多样性部门）；Institute of Forest Growth（森林生长研究所）；Eberswalde University of Sustainable Development（埃伯斯瓦尔德可持续发展大学）；State Forestry Research Centre（州林业研究机构）；Hasso Plattner Institute for Digital Engineering（哈索·plattnner数字工程研究所）
